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Latest Corporate legal affairs News
Minnesota attorney general sues 2 companies over COVID tests
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AUSTIN, Texas (AP) — Texas' attorney general on Wednesday sued Google, alleging the company asked local radio DJs to record personal endorsements for smartphones that they hadn't used or been provided. In the lawsuit filed in Montgomery County, Republican Attorney General Ken...
New York AG says Trump's company misled banks, tax officials
NEW YORK (AP) — The New York attorney general says her investigators have uncovered evidence that former President Donald Trump's company used "fraudulent or misleading" valuations of its golf clubs, skyscrapers and other property to get loans and tax benefits. In a court...
What comes next in New York's investigation of Donald Trump
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New York investigators tell court Trump's company used "fraudulent or misleading" documents to get loans, tax breaks
NEW YORK (AP) — New York investigators tell court Trump's company used "fraudulent or misleading" documents to get loans, tax breaks.
Microsoft buys game maker Activision Blizzard for about $70B
Microsoft is paying the enormous sum of nearly $70 billion for Activision Blizzard, the maker of Candy Crush and Call of Duty, a deal that would immediately make it a larger video-game company than Nintendo while raising questions about the deal's possible anti-competitive effects. ...
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US regulators aiming at illegal and anticompetitive mergers
WASHINGTON (AP) — U.S. competition regulators have mounted an effort to tighten enforcement against illegal mergers, in line with President Joe Biden's mandate for greater scrutiny to big business combinations. The Justice Department and the Federal Trade Commission announced...
Freed Missouri inmate sues over medical care in prison
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SPRINGFIELD, Ill. (AP) — The family of a delivery driver who died last month when a tornado collapsed the central Illinois Amazon facility where he worked filed a wrongful death lawsuit Monday in Madison County. The action on behalf of Austin McEwen, 26, claims that Amazon failed...


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Olympus' Social IN management philosophy is based on the three "Ins" of INtegrity, INnovation and INvolvement, defining the relationship between the company and society.
In 2004, Olympus Corporation joined the UN Global Compact, a voluntary initiative to create a global framework within which companies can contribute to good corporate citizenship and sustainable growth through responsible and creative leadership. Global Compact principles are incorporated into Olympus' Corporate Conduct Charter and Code of Conduct and reflected in the company's business activities and organization.
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YOKOTA AIR BASE, Japan — Five Department of Defense Education Activity Pacific school students became eligible to compete in the 53rd National Junior Science and Humanities following their Pacific-wide JSHS Symposium performance at Yokota AB in mainland Japan March 24-25.
Seoul American High School students Paul Lee, Brian Lee, Rikki Kendall and Kyelee Fitts (1st, 3rd, 4th and 5th places, respectively) and Kadena HS student Olivia Leavitt (2nd place) went on to compete in the National JSHS finals April 29 to May 2 in Hunt Valley, Md.
JSHS is designed to challenge and engage high school students in science, technology, engineering and mathematics (STEM). The keynote speaker for the Pacific-wide symposium was Dr. Caroline Benton, Tsukuba University vice president. Tsukuba University is a renowned scientific research facility located about 36 miles northeast of Tokyo. Dr. Benton attended a DoD school as a child and is an advocate for students and STEM-related programs. In addition to being the keynote speaker, Dr. Benton also served on the judging panel during the symposium.
Students from all over the Pacific came together to compete for a spot in the National competition in Washington, DC. The top five Pacific students went on to face regional winners from around the U.S., in an attempt to earn recognition and scholarships for their hard work. Each student also received recognition by the U.S. Army, Navy, and Air Force for the top STEM presentations.
The Effect of Dietary and Supplementary Antioxidants on the Respiration Rate of Saccharomyces cerevisiae.
The JSHS is a tri-service program funded by the Army, Navy and Air Force that encourages high school students to conduct original research in STEM. JSHS sponsors include: Office of the Assistant Secretary of the Army (Acquisition, Logistics & Technology); Office of Naval Research, Arlington, VA; and Air Force Office of Scientific Research, Washington, DC, in cooperation with higher education. The National JSHS Program is administered by the Academy of Applied Science, a non-profit educational organization located in Concord, New Hampshire. Details on the JSHS Regional and National events are available at http://www.jshs.org/.
The Army Educational Outreach Program (AEOP) is comprised of Army-sponsored research, education, competitions, internships and practical experiences designed to engage and guide students as well as teachers in science, technology, engineering, and mathematics (STEM). From elementary school through graduate school, students of all proficiency levels, interests, ethnic, economic and academic backgrounds are encouraged to participate in real world experiences involving these important disciplines. More information is available at http://www.usaeop.com/, at the Twitter handle @USAEOP and at www.facebook.com/goAEOP.
The Academy of Applied Science was founded in 1963 as a non-profit 501 (c)(3) to operate exclusively for charitable, educational and/or scientific purposes. The Academy has demonstrated over 50 years' experience in the effective delivery and administration of youth science outreach programs designed to support and encourage the future pool of trained STEM talent available to contribute to the nation's scientific and technological progress and to the military's research enterprise. The Academy supports students and teachers nationwide to achieve success in STEM through competitions, STEM enrichment activities for students and teachers, curriculum support for teachers, publications, mentorships, and awards programs.


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Q: Capistrano how can i use two deploy.rb files I'm working with project on local machine where internet connection is so slowly. For best testing i have another server in other place with fast internet connection. To test my projects i'm use capistrano with puma.
But my client use production environment on his server on capistrano with unicorn.
Question:
How can i divide my puma_deploy.rb and (unicorn_)deploy.rb for work with both capistrano deployments?
P.S. when i push repo to client, i add puma_deploy.rb to .gitignore, to prevent confuse with clients administrators, which makes deploy from own repo to own server.

A: It sounds like you could use the Capistrano Multistage extensions to manage your deployments in different environments.
You could set up your environment (the one you use for testing) as a "staging" environment.
However, I think it might be a better idea to just use the same thing for both your testing/staging environment as production. If there is a problem with the webserver or its configuration, you might not know until it goes to production, then it is sad faces all around trying to track down the issue.

redpajama_set_name: RedPajamaStackExchange


Sometimes a person applying for a Temporary Resident Visa (TRV) to visit Canada is asked to provide a letter of invitation from someone in Canada. The following information will help you prepare such a letter to send to a relative or friend abroad.
Please note, you CANNOT apply for a family member or friend who is outside Canada. The incoming visitor must initially apply to an immigration office in the country in which they are present and have been lawfully admitted; or the visitor's country of nationality or, if the visitor is stateless, their country of habitual residence other than a country in which they are residing without having been lawfully admitted [Immigration Refugee Protection Regulation, Section 11(2)(a)(b)].
A letter of invitation does not guarantee that a visa will be issued. Visa officers assess the applicant to determine whether they meet the requirements of the Immigration and Refugee Protection Act.
The following information is only a guide. You must write the letter of invitation yourself. Some visa offices may require that your letter be notarized by a Notary Public. Check with the visa office that is processing the application to find out if you need to do this. By writing a letter of invitation, you are not legally responsible for the visitor once they get to Canada, but you should provide the letter in good faith. You must give truthful information and intend to keep the promises you made in the letter.
Send your letter (notarized if necessary) to the person you are inviting to Canada. They must then submit this letter to the Canadian Embassy or Consulate outside of Canada when they apply for their Temporary Resident Visa. The letter of invitation is only one of the required supporting documents.
For more information, visit Citizenship and Immigration Canada's website on visiting Canada.


I am looking for a way to be able to attach an Outlook item (an email message) in an ASP.NET 3.5 application. Similar to when in Outlook and sending an attachment, you can select either a file, or an item. Selecting an Item, opens up your Outlook folder directory, and you can select an email message, and send it as an attachment. I have a need for being able to access these emails in the Outlook folder directory and add them as a file attachment. I am not using the File Upload Control, I am using an HTML input tag with type="file". Please let me know your ideas. Thanks!
The issue: renaming array keys inside a foreach loop.


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\section{Appendix}
\subsection{Visualizing Behavior: Multi-Room Object Manipulation Environment}
\begin{figure}[!h]
    \centering
    \includegraphics[width=0.8\linewidth]{MinigridPanel.png}
    \caption{Example task with corrections. Instruction: The agent receives the initial instruction. Correction 1: The agent mistakenly goes into the gray door, so it receives the correction to enter the green room, where the purple ball is located. Correction 2: The agent successfully picks up the ball, but then mistakenly enters the blue room, so it receives the correction to enter the red room, where the goal is located. Correction 3: The agent brings the object to the goal and solves the task.}
    \label{fig:tasks}
\end{figure}
An example task with corrections in show in Figure \ref{fig:tasks}.

\begin{figure}[!h]
    \centering
    \includegraphics[width=0.75\linewidth]{MinigridFailurePanel.png}
    \caption{Failure example. The orange arrow shows the task, the white arrows show the net trajectory.}
    \label{fig:minigrid_failure}
\end{figure}
It is possible to visualize failure cases, which illuminate the behavior of the algorithm on challenging tasks. In the failure case in Figure \ref{fig:minigrid_failure}, we note that the agent is able to successfully enter the purple room, pickup the green ball, and exit. However, after it receives the fourth correction telling it to go to the green goal, it forgets to pick up the green ball.  This behavior can likely be improved by varying corrections more at training time, and providing different corrections if an agent is unable to comprehend the first one.

\begin{figure}[!h]
    \centering
    \includegraphics[width=0.75\linewidth]{MinigridSuccessPanel.png}
    \caption{Success example. The orange arrow shows the task.}
    \label{fig:minigrid_success}
\end{figure}
Additionally, we present a success case in Figure \ref{fig:minigrid_success}, where the agent successfully learns to solve the task through iterative corrections, making further progress in each frame.

\subsection{Visualizing Behavior: Robotic Object Relocation Environment}
\begin{figure}[!h]
    \centering
    \includegraphics[width=0.75\linewidth]{PusherFailurePanel.png}
    \caption{Failure example. The orange arrow shows the task, the white arrows show the net trajectory.}
    \label{fig:pusher_failure}
\end{figure}
Similarly, we can again visualize failure cases for the robotic object relocation environment. In the failure case in Figure \ref{fig:pusher_failure}, the agent pushes the correct object, and very nearly solves the task after the first and second corrections. However, after it receives the third and fourth corrections telling it to go near the green block, its trajectory changes such that its path to the goal is blocked by the green block itself. This case is particularly challenging, as the combination of the bulky agent body, angle of approach, and the proximity of the goal location to the obstacle near it make the goal location inaccessible. Were the goal location a little further from the green block, or the agent of a form lending itself to nimbler motion, this task may have been solved by the second correction.

\begin{figure}[!h]
    \centering
    \includegraphics[width=0.75\linewidth]{PusherSuccessPanel.png}
    \caption{Success example. The orange arrow shows the task.}
    \label{fig:pusher_success}
\end{figure}
We also present a success case in Figure \ref{fig:pusher_success}, where the agent starts off pushing the wrong block, but successfully learns to solve the task through iterative corrections, making further progress in each frame.

\subsection{Training and Architecture Details}
\label{app:training_details}
We detail the training and architecture details for each environment. We use Adam for optimization with a learning rate of 0.001. The data buffer size is $1e6$. We train on the whole data buffer for five epochs each time before adding more data to the buffer. Unless otherwise stated, we use ReLU activations. MLP(32, 32) specifies a multilayer-perceptron with 2 layers each of size 32. CNN((4, 2x2, 1), (4, 2x2, 1)) specifies a 2 layer convolutional neural network where each layer has 4 filters, 2x2 kernels, and 1 stride.

To train the expert policies we use Proximal Policy Optimization with a dense reward function. For the multi-room domain we keep track of the next subgoal the agent has to complete. The reward is $-0.01 *$ Euclidian distance to the next subgoal where a grid is a distance of 1. If the agent completes a previously uncompleted subgoal it gets a onetime reward of 100. For the robotic object manipulation domain the reward is -(Euclidian distance of the gripper to the block + $5 *$ Euclidian distance of the block to the target location).

For the multi-room domain we meta-train on 1700 environments. Our method converges in 6 DAgger steps so it takes 30 corrections per environment for a total of 51,000 corrections. For the robotic object relocation domain, we train on 750 environments. Our method converges in 9 DAgger steps so it takes 45 corrections per environment for a total of 33,750 corrections.
\subsubsection{Multi-room object manipulation}
The observation is a 7x7x4 grid centered on the agent. The $1^\text{st}$ channel encode object types (empty, wall,  closed door, locked door, triangle, square, circle, goal). The $2^\text{nd}$ channel encodes colors (none, blue, green, gray, purple, red, yellow). The $3^\text{rd}$ and $4^\text{th}$ channels also encode object types and colors respectively but only for objects the agent is currently holding. The observation also includes a binary indicatory if the agent is currently holding an object. The action space is of size 6 and consists of (move up, move left, move right, move down, pickup object, drop object). The length of a trajectory is 100.

\textbf{Correction Module} Each previous trajectory is first subsampled every $25^\text{th}$ state. Each observation is then processed by a 2D CNN((4, 2x2, 1), (4, 2x2, 1)) followed by a MLP(32, 32). This trajectory of state embeddings is then processed by a 1D CNN(4, 2, 1) followed by a MLP(16, 4).

The language correction is converted into word embeddings of size 16 and processed by a 1D CNN(4, 2, 1) followed by a MLP (16, 4). For a given correction iteration, the trajectory embedding and the correction embedding are then concatenated and fed through a MLP(32 ,32). These tensors are then averaged across the number of correction iterations.

\textbf{Instruction Module}
The instruction is converted into word embeddings (the same embeddings as the correction) of size 16 and processed by a 1D CNN(4, 2, 1) followed by a MLP(16, 32).

\textbf{Policy Module}
The observation is processed by a 2D CNN((8, 2x2, 1), (8, 2x2, 1)) followed by a MLP(32, 32). This state embedding, the correction module tensor, and the instruction module tensor are concatenated and fed through a MLP (64, 64) to output an action distribution.

\subsubsection{Robotic object relocation}
The observation is of size 19 and includes the (x, y, orientation) of the gripper and the  (x, y) coordinates of the 3 movable blocks and 5 fixed blocks. The action space is of size 4 and consists of applying force on the gripper in the cardinal directions. The length of a trajectory is 350.

\textbf{Correction Module}
Each previous trajectory is first subsampled every $70^\text{th}$ state. The trajectory is then processed by a 1D CNN(8, 2, 1) followed by a MLP(16, 8).

The language correction is converted into word embeddings of size 16 and processed by a 1D CNN(4, 2, 1) followed by a MLP(16, 16). For a given correction iteration, the trajectory embedding and the correction embedding are then concatenated and fed through a MLP(32 ,32). These tensors are then averaged across the number of correction iterations.

\textbf{Instruction Module}
The instruction is converted into word embeddings (the same embeddings as the correction) of size 16 and processed by a 1D CNN(4, 2, 1) followed by a MLP(16, 16).

\textbf{Policy Module}
The observation, the correction module tensor, and the instruction module tensor are concatenated and fed through a MLP(256, 256, 256) to output an action distribution.
\section{Introduction}

Behavioral skills or policies for autonomous agents are typically specified in terms of reward functions (in the case of reinforcement learning) or demonstrations (in the case of imitation learning). However, both reward functions and demonstrations have downsides as mechanisms for communicating goals. Reward functions must be engineered manually, which can be challenging in real-world environments, especially when the learned policies operate directly on raw sensory perception. Sometimes, simply defining the goal of the task requires engineering the very perception system that end-to-end deep learning is supposed to acquire. Demonstrations sidestep this challenge, but require a human demonstrator to actually be able to perform the task, which can be cumbersome or even impossible. When humans must communicate goals to each other, we often use language. Considerable research has also focused on building autonomous agents that can follow instructions provided via language (\cite{branavan, chenmooney}). However, a single instruction may be insufficient to fully communicate the full intent of a desired behavior. For example, if we would like a robot to position an object on a table in a particular place, we might find it easier to guide it by telling it which way to move, rather than verbally defining a coordinate in space. Furthermore, an autonomous agent might be unable to deduce \emph{how} to perform a task from a single instruction, even if it is very precise. In both cases, interactive and iterative corrections can help resolve confusion and ambiguity, and indeed humans often employ corrections when communicating task goals to each other.

In this paper, our goal is to enable an autonomous agent to accept instructions and then iteratively adjust its policy by incorporating interactive \emph{corrections}  (illustrated in Figure \ref{fig:teaser}). This type of in-the-loop supervision can guide the learner out of local optima, provide fine-grained task definition, and is natural for humans to provide to the agent. As we discuss in Section~\ref{sec:related}, iterative language corrections can be substantially more informative than simpler forms of supervision, such as preferences, while being substantially easier and more natural to provide than reward functions or demonstrations.

In order to effectively use language corrections, the agent must be able to ground these corrections to concrete behavioral patterns. We propose an end-to-end algorithm for grounding iterative language corrections by using a multi-task setup to \emph{meta-train} a model that can ingest its own past behavior and a correction, and then correct its behavior to produce better actions. During a meta-training phase, this model is iteratively retrained on its own behavior (and the corresponding correction) on a wide distribution of known tasks. The model learns to correct the types of mistakes that it actually tends to make in the world, by interpreting the language input. At meta-test time, this model can then generalize to new tasks, and learn those tasks quickly through iterative language corrections.

The main contributions of our work are the formulation of guided policies with language (GPL) via meta-learning, as well as a practical GPL  meta-learning algorithm and model. We train on English sentences sampled from a hand-designed grammar as a first step towards real human-in-the-loop supervision. We evaluate our approach on two simulated tasks - multi-room object manipulation and robotic object relocation. The first domain involves navigating a complex world with partial observation, seeking out objects and delivering them to user-specified locations. The second domain involves controlling a robotic gripper in a continuous state and action space to move objects to precise locations in relation to other objects. This requires the policy to ground the corrections in terms of objects and places, and to control and correct complex behavior.

\begin{figure}[t]
    \centering
    \includegraphics[width=0.8\linewidth]{newlanguagepicv2.png}
    \caption{An example where corrections disambiguate an instruction. The agent is unable to fully deduce the user's intent from the instruction alone and iterative language corrections guide the agent to the correct position. Our method is concerned with meta-learning policies that can ground language corrections in their environment and use them to improve through iterative feedback.
    }
    \label{fig:teaser}
\end{figure}

\section{Related Work}
\label{sec:related}
Tasks for autonomous agents are most commonly specified by means of reward functions~\citep{sutton} or demonstrations~\citep{Argall}. Prior work has studied a wide range of different techniques for both imitation learning~\citep{ziebart, irlabbeel} and reward specification, including methods that combine the two to extract reward functions and goals from user examples~\citep{vice, gatech} and demonstrations~\citep{airl, ifo}. Other works have proposed modalities such as preferences~\citep{preferences} or numerical scores~\citep{tamer}. Natural language presents a particularly appealing modality for task specification, since it enables humans to communicate task goals quickly and easily. Unlike demonstrations, language commands do not require being able to perform the task. Unlike reward functions, language commands do not require any manual engineering. Finally, in comparison to low-bandwidth supervision modalities, such as examples of successful outcomes or preferences, language commands can communicate substantially more information, both about the goals of the task and how it should be performed.

A considerable body of work has sought to ground natural language commands in meaningful behaviors. These works typically use a large supervised corpus in order to learn policies that are conditioned on natural language commands~\citep{macmahon, branavan,  vogel2010spatial, chenmooney, stefanierobot, artzizettle, kimmooney, andreasinstr, mirsa, L3, honglak}. Other works consider using a known reward function in order to learn how to ground language into expert behaviors~\citep{janner, andreasinstr}.  Most of these works consider the case of instruction following. However, tasks can often be quite difficult to specify with a single language description, and may require interactive guidance in order to be achieved. We focus on this setting in our work,  where the agent improves its behavior via iterative language corrections.

While the focus in our work is on incorporating language corrections, several prior works have also studied reinforcement learning and related problems with in-the-loop feedback of other forms \cite{akrour,pilarski,akrour2012,elasri,wang16, tamer, sidrss}. In contrast, we study how to incorporate language corrections, which are more natural for humans to specify and can carry more information about the task. However, language corrections also present the challenge that the agent must learn how to ground them in behavior. To this end, we introduce an end-to-end algorithm that directly associates language with changes in behavior without intermediate supervision about object identities or word definitions.

Our approach to learning to learn from language corrections is based on meta-reinforcement learning. In meta-reinforcement learning, a meta-training procedure is used to learn a procedure (represented by initial network weights or a model that directly ingests past experience)~\citep{schmidhuber} that can adapt to new tasks at meta-test time. However, while prior work has proposed meta-reinforcement learning for model-free RL~\citep{learning_to_rl,rl2,maml, tcml}, model-based RL~\citep{anusha_ignasi}, a wide range of supervised tasks~\citep{prototype, mann, matching, metacritic}, as well as goal specification~\citep{Xu2018LearningAP, flo}, to our knowledge no prior work has proposed meta-training of policies that can acquire new tasks from iterative language corrections.

\section{Problem Formulation}
\label{sec:problem}

We consider the sequential decision making framework, where an agent observes states $s \in \mathcal{S}$, chooses to execute actions $a \in \mathbf{A}$ and transitions to a new state $s'$ via the transition dynamics $\mathcal{T}(s'|s,a)$. For goal directed agents, the objective is typically to learn a policy $\pi_{\theta}$ that chooses actions enabling the agent to achieve the desired goal. In this work, the agent's goal is specified by a language instruction $L$. This instruction describes what the general objective of the task is, but may be insufficient to fully communicate the intent of a desired behavior.

The agent can attempt the task multiple times, and after each attempt, the agent is provided with a language \emph{correction}. Each attempt results in a trajectory $\tau = (s_0, a_0, s_1, a_1, ...., s_T, a_T)$, the result of the agent executing its policy $\pi_{\theta}(a|s, L)$ in the environment. After each attempt, the user generates a correction according to some unknown stochastic function of the trajectory $c \sim \mathcal{F}(\tau)$. Here, c is a language phrase that indicates how to improve the current trajectory $\tau$ to bring it closer to accomplishing the goal. This process is repeated for multiple trials, and we will use $\tau_i$ to denote the trajectory on the $i^\text{th}$ trial, and $c_i$ to denote the corresponding correction. An effective model should be able to incorporate these corrections to come closer to achieving the goal. This process is illustrated in Figure~\ref{fig:teaser}.

In the next section, we will describe a model that can incorporate iterative corrections, and then describe a meta-training procedure that can train this model to incorporate corrections effectively.

\section{The Language-Guided Policy Learning Model}

As described in Section~\ref{sec:problem}, our model for guiding policies with language (GPL) must take in an initial language instruction, and then iteratively incorporate corrections after each attempt at the task. This requires the model to ground the contents of the correction in the environment, and also interpret it in the context of its own previous trajectory so as to decide which actions to attempt next. To that end, we propose a deep neural network model, shown in Figure~\ref{fig:model}, that can accept the instruction, correction, previous trajectory, and state as input. The model consists of three modules: an instruction following module, a correction module, and a policy module.

\begin{figure}[!h]
    \centering
    \includegraphics[width=0.8\linewidth]{LanguageFigurev4.png}
    \caption{The architecture of our model. The instruction module embeds the initial instruction $L$, while the correction modules embed the trajectory $\tau_i$ and correction $c_i$ from each previous trial. The features from these corrections are pooled and provided to the policy, together with the current state $s$ and the embedded initial instruction.}
    \label{fig:model}
\end{figure}

The instruction following module interprets the initial language instruction. The instructions are provided as a sequence of words which is converted into a sequence of word-embeddings. A 1D CNN processes this sequence to generate an instruction embedding vector $z_{im}$ which is fed into the policy module.

The correction module interprets the previous language corrections $\lowercase{\mathbf{C}}_n = (c_0,\dots,c_n)$ in the context of the previous trajectories $\lowercase{\boldsymbol{\tau}}_n=(\tau_{0}.\dots,\tau_{n})$. Each previous trajectory is processed by a 1D CNN to generate a trajectory embedding. The correction $c_i$, similar to the language description, is converted into a sequence of word-embeddings which is then fed through a 1D CNN. The correction and trajectory embeddings are then concatenated and transformed by a MLP to form a single tensor. These tensors are then averaged to compute the full correction history tensor $z_{cm} = \frac{1}{n} \sum_{j=0}^n \text{MLP}(\text{1dCNN}(\tau_{j}), \text{1dCNN}(c_j))$.

The policy module has to integrate the high level description embedded into $z_{im}$, the actionable changes from the correction module $z_{cm}$, and the environment state $s$, to generate the right action. This module inputs $z_{cm}$, $z_{im}$ and $s$ and generates an action distribution $p(a|s)$ that determine how the agent should act. Specific architecture details are described in the appendix.

Note that, by iteratively incorporating language corrections, such a model in effect implements a \emph{learning algorithm}, analogously to meta-reinforcement learning recurrent models proposed in prior work that read in previous trajectories and rewards~\citep{rl2,learning_to_rl}. However, in contrast to these methods, our model has to use the language correction to improve, essentially implementing an interactive, user-guided reinforcement learning algorithm. As we will demonstrate in our experiments, iterative corrections cause this model to progressively improve its performance on the task very quickly. In the next section, we will describe a meta-learning algorithm that can train this model such that it is able to adapt to iterative corrections effectively at meta-test time.
\begin{figure}[!h]
    \centering
    \includegraphics[width=0.45\linewidth]{trainingschematic.png}
    \hspace{5mm}
    \includegraphics[width=0.45\linewidth]{trainingdatacollection.png}
    \caption{\textbf{Left:} Overall training procedure.
    We collect data for each task $[1,2,\dots,N]$ using DAgger, storing it in the data buffer. This is used to train a GPL  policy with supervised learning. The trained policy is then used to collect data for individual tasks again, repeating the process until convergence. \textbf{Right:} Individual data collection procedure for a single task. The GPL  policy is initially executed to obtain a trajectory $\tau_1$. This trajectory is corrected by an expert $\pi^*$ to generate data to be added to the buffer. The trajectory is then used to generate a correction, which is fed back into $\pi$, along with $\tau_1$ to generate a new trajectory $\tau_2$. This repeats until a maximum number of corrections is given, adding data to the buffer at each step.}
    \label{fig:train_method}

\end{figure}

\setlength{\textfloatsep}{10pt}

\section{Meta-Training the GPL  Model to Learn From Corrections}
In order for the GPL  model to be able to learn behaviors from corrections, it must be meta-trained to understand both instructions and corrections properly, put them in the context of its own previous trajectories, and associate them with objects and events in the world. For clarity of terminology, we will use the term ``meta-training'' to denote the process of training the model, and ``meta-testing'' to denote its use for solving a new task with language corrections.

During meta-training, we assume access to samples from a distribution of meta-training tasks $T \sim p(T)$. The tasks that the model will be asked to learn at meta-test time are distinct from the meta-training tasks, though we assume them to be drawn from the same distribution, which is analogous to the standard distribution assumption in supervised learning. The tasks have the same state space $\mathcal{S}$ and action space $\mathbf{A}$, but each task has a distinct goal, and each task $T$ can be described by a different language instruction $L_T$. In general, more than one instruction could describe a single task, and the instructions might contain ambiguity.

Each of the tasks during meta-training has a ground truth objective provided by a reward function. We can use the reward function with any existing reinforcement learning algorithm to train a near-optimal policy. Therefore, we derive the algorithm for the case where we have access to a near-optimal policy $\pi_T^*(a|s)$ for each task $T$. In our experiments, $\pi_T^*(a|s)$ is obtained via reinforcement learning from ground truth rewards. For each meta-training task, we also assume that we can sample from the corresponding correction function $\mathcal{F}_T(c|\tau)$, which generates a correction $c$ for the trajectory $\tau$ in the context of task $T$. In practice, these corrections might be provided by a human annotator, though we use a computational proxy in our experiments. The key point is that we can use rewards to transform a collection of task-specific policies learned offline into a fast online learner during test time.

By using $\pi_T^*(a|s)$, $L_T$, and $\mathcal{F}_T(\tau)$, we can train our model for each task by using a variant of the DAgger algorithm~\citep{dagger}, which was originally proposed for single-task imitation learning, where a learner policy is trained to mimic a near-optimal expert.
We extend this approach to the setting of meta-learning, where we use it to meta-train the GPL  model.
Starting from an initialization where the previous trajectory $\tau_{0}$ and correction $c_0$ are set to be empty sequences, we repeat the following process: first, we run the policy corresponding to the current learned model $\pi(a|s, L_T, \lowercase{\boldsymbol{\tau}}_{0}, \lowercase{\mathbf{C}}_0)$ to generate a new trajectory $\tau_1$ for the task $T$. Every state along $\tau_1$ is then labeled with near-optimal actions by using $\pi_T^*(a|s)$ to produce a set of training tuples $(L_T,\tau_{0},c_0,s,a^*)$. These tuples are appended to the training set $\mathcal{D}$. Then, a correction $c_{1}$ is sampled from $\mathcal{F}_T(c|\tau_{1})$, and a new trajectory is sampled from $\pi(a|s, L_T, \lowercase{\boldsymbol{\tau}}_{1}, \lowercase{\mathbf{C}}_{1})$. This trajectory is again labeled by the expert and appended to the dataset.
In the same way, we iteratively populate the training set $\mathcal{D}$ with the states, corrections, and prior trajectories observed by the model, all labeled with near-optimal actions. This process is repeated for a fixed number of corrections or until the task is solved, for each of the meta-training tasks.
The model is then trained to maximum the likelihood of the samples in the dataset $\mathcal{D}$. Then, following the DAgger algorithm, the updated policy is used to again collect data for each of the tasks, which is appended to the dataset and used to train the policy again, until the process converges or a fixed number of iterations. This algorithm is summarized in Algorithm~\ref{alg:metatrain} and Figure~\ref{fig:train_method}.

\section{Learning New Tasks with The GPL  Model}

Using a GPL  model meta-trained as described in the previous section, we can solve new ``meta-testing'' tasks $T_{test} \sim p(T)$ drawn from the same distribution of tasks with interactive language corrections. An initial instruction $L_T$ is first provided by the user, and the procedure for adapting with corrections follows the illustration in Figure~\ref{fig:teaser}. The learned policy is initially rolled out in the environment conditioned on $L_T$, and with the previous trajectory $\tau_{0}$ and correction $c_0$ initialized to the empty sequence. Once this policy generates a trajectory $\tau_1$, we can use the correction function $\mathcal{F}_T$ to generate a correction $c_1 = \mathcal{F}_T(\tau_1)$. The trajectory $\tau_1$, along with the correction $c_1$ gives us a new improved policy which is conditioned on $L_T$, $\lowercase{\boldsymbol{\tau}}_1$, and $\lowercase{\mathbf{C}}_1$. This policy can be executed in the environment to generate a new trajectory $\tau_2$, and the process repeats until convergence, thereby learning the new task. We provide the policy with the previous corrections as well but omit in the notation for clarity. This procedure is summarized in Algorithm~\ref{alg:metatest}.

This procedure is similar to meta-reinforcement learning \citep{maml, rl2}, but uses grounded natural language corrections in order to guide learning of new tasks with feedback provided in the loop. The advantage of such a procedure is that we can iteratively refine behaviors quickly for tasks that are hard to describe with high level descriptions. Additionally, providing language feedback iteratively in the loop may reduce the overall amount of supervision needed to learn new tasks. Using easily available natural language corrections in the loop can change behaviors much more quickly than scalar reward functions.

\begin{minipage}[t]{6.5cm}
\vspace{-10pt}
\begin{algorithm}[H]
\LinesNumbered
\DontPrintSemicolon
\SetAlgoLined
 Initialize data buffer $\mathcal{D}$\;
 \For{iteration $j$}{
    \For{task $T$}{
        Initialize $\tau_{0} = 0$ and $c_0 = 0$ \;
        \For{corr iter $i \in\{0,...,c_{max} \}$}{
            Execute $\pi(a | s, L_T, \lowercase{\boldsymbol{\tau}}_{i}, \lowercase{\mathbf{C}}_i)$ on $T$ to collect $\tau_{i+1}$\;
            Obtain $c_{i+1} \sim \mathcal{F}_T(\tau_{i+1})$ \;
            Label $a^\star \sim \pi^\star_T(a|s)$, $\forall \,\, s \in \tau_{i+1}$\;
            Add all $(L_T,\tau_i,c_i,s,a^\star)$ to $D$\;
        }
    }
    Train $\pi$ on $\mathcal{D}$.
 }

\caption{GPL  meta-training algorithm.}
\label{alg:metatrain}
\end{algorithm}
\end{minipage}%
\hspace{0.5cm}
\begin{minipage}[t]{6.5cm}
\vspace{-10pt}
\begin{algorithm}[H]
\DontPrintSemicolon
\SetAlgoLined
Given new task $T_i$, with instruction $L_T$\;
Initialize $\tau_0 = 0$ and $c_0 = 0$ \;
\For{corr iter $i\in\{0,...,c_{max} \}$}{
    Execute $\pi(a | s, L_T, \lowercase{\boldsymbol{\tau}}_i, \lowercase{\mathbf{C}}_i)$ on $T$ to collect $\tau_{i+1}$ \;
    Obtain $c_{i+1} \sim \mathcal{F}_T(\tau_{i+1})$ \;
 }
 \caption{Meta-testing: learning new tasks with the GPL  model.}
 \label{alg:metatest}
\end{algorithm}
\end{minipage}

\section{Experiments}
Our experiments analyze GPL  in a partially observed object manipulation environment and a block pushing environment. The first goal of our evaluation is to understand where GPL  can benefit from iterative corrections -- that is, does the policy's ability to succeed at the task improve as each new correction provided. We then evaluate our method comparatively, in order to understand whether iterative corrections provide an improvement over standard instruction-following methods, and also compare GPL  to an oracle model that receives a much more detailed instruction, but without the iterative structure of interactive corrections. We perform further experiments including ablations to evaluate the importance of each module and additional experiments with more varied corrections. We also compare our method to state of the art instruction following methods, other baselines which use intermediate rewards instead of corrections, and pretraining with language. Our code and supplementary material will be available at \url{https://sites.google.com/view/lgpl/home}

\subsection{Experimental Setup}
We describe the two experimental domains that we evaluate this method on. The first task is underspecified while the second task is ambiguous so each task lends itself to the use of corrections.

\subsubsection{Multi-room Object Manipulation}
Our first environment is discrete and represents the floor-plan of a building with six rooms (Figure~\ref{fig:instruction_correction}), based on \citet{gym_minigrid}. The task is to pickup a particular object from one room and bring it to a goal location in another room. Each room has a uniquely colored door that must be opened to enter the room, and rooms contain objects with different colors and shapes. Actions are discrete and allow for cardinal movement and picking up and dropping objects. The environment is partially observed: the policy only observes the contents of an ego-centric 7x7 region centered on the present location of the agent, and does not see through walls or closed doors. The contents of a room can only be observed by first opening its door.

\begin{wrapfigure}{R}{0.3\textwidth}
    \centering
    \includegraphics[width=\linewidth]{MinigridLabeled.png}
    \caption{The multi-room object manipulation environment with labeled components.}
    \label{fig:instruction_correction}
    \vspace{-6mm}
\end{wrapfigure}

This environment allows for the natural language instruction which specifies the task to be underspecified. The instruction is given as "Move <goal object color> <goal object shape> to <goal square color> square". Since the agent cannot see into closed rooms, it does not initially know the locations of the goal object or goal square. It must either explore or rely on external corrections which guide the agent to the appropriate rooms.

Environments are generated by sampling a goal object color, goal object shape, and goal square color which are placed at random locations in different random rooms. There are 6 possible colors and 3 possible object shapes. Decoy objects and goals are placed randomly among the six rooms. The only shared aspect between tasks are the color of the doors so the agent must learn to generalize across a variety of different objects across different locations.

To generate the corrections, we describe a task as a list of subgoals that the agent must complete. For example, the instruction in Figure \ref{fig:instruction_correction} is "Move green triangle to green square", and the subgoals are "enter the blue room", "pick up the green triangle", "exit the blue room", "enter the purple room", and "go to the green goal". The correction for a given trajectory is then the first subgoal that the agent failed to complete. The multistep nature of this task also makes it challenging, as the agent must remember to solve previously completed subgoals while incorporating the corrections to solve the next subgoal.

The training and test tasks are generated such that for any test task, its list of all five subgoals does not exist in the training set. There are 3240 possible lists of all five subgoals. We train on 1700 of these environments and reserve a separate set for testing.
\subsubsection{Robotic Object Relocation}

\begin{wrapfigure}{R}{0.28\textwidth}
    \centering
    \includegraphics[width=\linewidth]{PusherLabeled.png}
    \caption{The robotic object relocation environment. The agent must push the red block right of the magenta block.}
    \label{fig:env2}
    \vspace{-6mm}
\end{wrapfigure}

The second environment is a robotic object relocation task built in Mujoco \citep{mujoco} shown in Figure \ref{fig:env2}. The task is to apply force on a gripper to push one of three blocks to a target location. Five immovable blocks scattered in the environment act as obstacles. States are continuous and include the agent and block positions. Actions are discrete and apply directional forces on the gripper.

The instruction is given as "Move <goal block color> close to <obstacle block color>," where the closest or $2^\text{nd}$ closest obstacle block to the target location is randomly chosen. This instruction is ambiguous, as it does not describe the precise location of the target. Corrections will help guide the agent to the right location relative to the obstacles and previous actions of the agent. Environments are generated by sampling one of the three movable blocks to push and sampling a random target location. We generate 1000 of these environments and train on 750 of them. There are three types of corrections, and feedback is provided by stochastically choosing between these types. The correction types are directional ("Move a little left", "Move a lot down right"), relational ("Move left of the white block", "Move closer to the pink block"), or indicate the correct block to push ("Touch the red block").

This environment was chosen because of its continuous state space and configuration of objects which allows for spatial relations between objects. It is more natural and convenient for a user to specify a goal with language, e.g., "close to the pink block," than using exact spatial coordinates. Furthermore, because the corrections can be relative to other blocks, the policy must learn to contextualize the correction in relation to other objects and its own previous behavior.

\subsection{Comparisons}
\label{sec:comparison}
We compare our method to two types of alternative methods - 1) alternative methods for instruction following 2) methods using rewards for finetuning on new tasks.

We first compare our method to a standard instruction following method using our architecture, a method that receives full information, and to an instruction following architecture from \cite{misra17} which we call MIVOA. We test MIVOA with both the instruction and full information. All methods are trained with DAgger. The instruction following methods only receive the instruction, which is ambiguous and does not contain the precise locations of objects or target positions. The full information methods receive the exact information the agent needs but for which a human may not always want to provide or have access to. For the multi-room environment the full information method receives \emph{all} the subgoals that are needed to solve the task, but does not receive them interactively. The full information method for the robotic object relocation task receives which block to move and the exact coordinates of the goal and so is an oracle. We measure the performance of an agent on the task by computing the completion rate: the fraction of subgoals (max 5) that the agent has successfully completed. The completion rate for the robotic domain is $1 - \frac{\text{final block dist}}{\text{initial block dist}}$. We expect our model to perform better than the instruction following baseline as it can receive the missing information through the corrections and we expect it to come close to performing as well as the full information method. The results for this comparison is shown in Table~\ref{tab:task_performanc}.

In the second set of comparisons, we compare the sample complexity of learning new tasks with GPL  against other baselines which utilize the reward function for learning new tasks (Figure \ref{fig:test_curves}). We compare against a reinforcement learning (RL) baseline that trains a separate policy per task using the reward. In addition, we run a baseline that does pretraining with DAgger on the training tasks and then finetunes on the test tasks with RL, thereby following a similar procedure as \cite{L3}. In both domains, the pretrained policy receives the instruction. For RL and finetuning we use the same algorithm \citep{PPO} and reward function used to train the expert policies. Additionally, in order to evaluate if the language corrections provide more information than just a scalar correction we also run a version of our method called GPR (Guiding Policies with Reward) which replaces the language correction with the reward. The correction for a trajectory is the sum of rewards of that trajectory. The performance of all of these methods is shown in Figure \ref{fig:test_curves}.

\subsection{Learning New Tasks Quickly with Language Corrections}
As described above, we consider two domains - multi-room object manipulation and a robotic object relocation task. In the object relocation domain, the test tasks here consist of new configurations of objects and goals. For the robotic domain, the test tasks consist of pushing to new target locations. Details on the meta-training complexity of our method are in appendix \ref{app:training_details}.

In the first set of comparisons mentioned in Section~\ref{sec:comparison}, we measure the completion rate of our method for various numbers of corrections on the test tasks. The instruction baseline does not have enough information and is unable to effectively solve the task. As expected, we see increasing completion rates as the number of corrections increases and the agent incrementally gets further in the task. For the multi-room domain our method matches the full information baseline with 3 corrections and outperforms it with 4 or more corrections. Since the full information baseline receives all 5 subgoals, this means our method performs better with \emph{less} information. The interactive nature of our method allows it to receive only the information it needs to solve the task. Furthermore, our model must learn to map corrections to changes in behavior which may be more modular, disentangled, and easier to generalize compared to mapping a long list of instructions to a single policy that can solve the task. For the robotic domain, our model exceeds the performance of the instruction baseline with just 1 correction. With more corrections it comes close to the full information method which receives the exact goal coordinates.

In the second set of comparisons mentioned in Section~\ref{sec:comparison}, we compare against a number of baselines that use the task reward (Fig \ref{fig:test_curves}). We see that our method can achieve high completion rate with very few test trajectories. While GPL  only receives up to five trajectories on the test task, the RL baseline takes more than 1000 trajectories to reach similar levels of performance. The RL baseline is able to achieve better final performance but takes orders of magnitude more training examples and has access to the test task reward. The pretraining baseline has better sample complexity than the RL baseline but still needs more than 1000 test trajectories. The reward guided version of our method, GPR, performs poorly on the multi-room domain but obtains reasonable performance for the robotic domain. This may indicate that language corrections in the multi-room domain provide much more information than just scalar rewards. However, using scalar rewards or binary preferences may be an alternative to language corrections for continuous state space domains such as ours.

\begin{table*}[!htb]
    \centering
    \resizebox{\textwidth}{!}{%
    \begin{tabular}{l|cccc|cccccc}

    \toprule
         Env &  Instruction & Full Info & MIVOA (Instr.)& MIVOA (Full Info) &$\lowercase{\mathbf{C}}_0$   &   $\lowercase{\mathbf{C}}_1$   &   $\lowercase{\mathbf{C}}_2$ & $\lowercase{\mathbf{C}}_3$ & $\lowercase{\mathbf{C}}_4$ & $\lowercase{\mathbf{C}}_5$\\
         \hline
         Multi-room      & 0.075  & 0.73            & 0.067    & 0.63  & 0.066  &   0.46 & 0.65 & 0.73 & 0.77 & \textbf{0.82} \\
         Obj Relocation  & 0.64   & \textbf{0.96}   & 0.65    & -  & 0.65  &   0.80 & 0.84 & 0.85 & 0.88 & 0.90 \\
    \bottomrule
    \end{tabular}}
    \caption{Mean completion rates on test tasks for baseline methods and ours across 5 random seeds. $\lowercase{\mathbf{C}}_i$ denotes that the agent has received $i$ corrections. GPL  is able to quickly incorporate corrections to improve agent behavior over instruction following with fewer corrections than full information on the multi-room domain. MIVOA is an architecture from \cite{misra17}.}
    \label{tab:task_performanc}
\end{table*}

\begin{figure}[h]
    \vspace{-4mm}
    \centering
    \includegraphics[width=0.35\linewidth]{minigrid_curve5.png}
    \includegraphics[width=0.35\linewidth]{pusher_curve5.png}
    \caption{Sample complexity on test tasks. The mean completion rate is plotted against the number of trajectories using during training per task. Our method (GPL) is shown in green.}
    \label{fig:test_curves}
\end{figure}

\subsection{Analyzing the Behavior of GPL }

\begin{table}[h]
\centering
    \begin{tabular}{l|cccccc}
    \toprule
         Ablations   & $\lowercase{\mathbf{C}}_0$   &   $\lowercase{\mathbf{C}}_1$   &   $\lowercase{\mathbf{C}}_2$ & $\lowercase{\mathbf{C}}_3$ & $\lowercase{\mathbf{C}}_4$ & $\lowercase{\mathbf{C}}_5$\\
         \hline
         Base       &   0.066  &   0.46 & 0.65 & 0.73 & 0.77 & 0.82    \\
         No instruction &  0.059  &   0.45 & 0.62 & 0.72 & 0.78 & 0.79 \\
         No trajectory &  0.077  &   0.44 & 0.62 & 0.70 & 0.76 & 0.77 \\
         Only immediate correction &  0.067  &   0.49 & 0.44 & 0.58 & 0.59 & 0.63 \\
    \bottomrule
    \end{tabular}
    \caption{Ablation Experiments analyzing the importance of various components of the model on the multi-room env. We see that removing previous corrections (only $c_i$) performs the worst, while removing instruction $L$ is less impactful.}
    \label{fig:ablation}
\end{table}
To understand GPL  better, we perform a number of different analyses - model ablations, extrapolation to more corrections, more varied corrections and generalization to out of distribution tasks.

We perform ablations on the multi-room domain to analyze the importance of each component of our model in Figure \ref{fig:ablation}. For the three ablations, we remove the instruction $L$, remove the previous trajectory $\tau_i$, and provide only the immediate previous correction $c_i$ instead of all previous corrections. We find that removing the instruction hurts the performance the least. This makes sense because the model can receive the information contained in the instruction through the corrections. Removing the previous corrections hurts the performance the most. Qualitatively, the agent that does not have access to the previous tends to forget what it had done previously and erases the progress it made. This explains the dip in performance from $c_1$ to $c_2$ for the only immediate correction.

\begin{wraptable}{r}{5.5cm}

    \resizebox{0.4\textwidth}{!}{%
    \begin{tabular}{l|ccc}
    \toprule
         Env  & $\lowercase{\mathbf{C}}_5$   &   $\lowercase{\mathbf{C}}_7$   &   $\lowercase{\mathbf{C}}_{10}$ \\
         \hline
         Multi-room &     0.82 & 0.83 & 0.86\\
         Obj Relocation & 0.90 & 0.91 & 0.95\\
    \bottomrule
    \end{tabular}}
    \caption{Mean completion rates on test tasks for 5, 7, 10 corrections. Only up to 5 corrections are seen during training.}
    \label{tab:extrap}
    \vspace{-3mm}
\end{wraptable}
We also investigate if performance continues to increase if we provide more corrections at meta-testing time than seen during training. In Table \ref{tab:extrap}, we provide up to 10 corrections during test time while only up to 5 corrections are seen during training. We see that completion rate increases from $0.82$ to $0.86$ for the multi-room domain and from $0.90$ to $0.95$ for the object relocation domain. These are small gains in performance and we expect to see diminishing returns as the number of corrections increases even further.

In Table \ref{tab:ambig} we investigate what happens if we give an agent more varied corrections in the multi-room environment. We add two new correction types. The first is directional and specifies which of the eight cardinal direction the next goal is in, e.g. "goal room is southwest." The second type is binary and consists of simple "yes/no" type information such as "you are in the wrong room".

\begin{table}[h]
\centering
    \begin{tabular}{l|cccccc}
    \toprule
         Type        &   $\lowercase{\mathbf{C}}_1$   &   $\lowercase{\mathbf{C}}_2$ & $\lowercase{\mathbf{C}}_3$ & $\lowercase{\mathbf{C}}_4$ & $\lowercase{\mathbf{C}}_5$\\
         \hline
         Directional   &   0.242   & 0.343  & 0.43  & 0.51  & 0.56 \\
         Binary  &    0.073 & 0.078  & 0.08  & 0.089  & 0.09 \\
         All   &   0.236  & 0.363  & 0.44  & 0.538  & 0.606  \\
    \bottomrule
    \end{tabular}
    \caption{Experiments investigating different correction types and effect on performance (mean completion). The experiments agree with intuition that binary carries little information and results in a small increase of the completion rate. Directional corrections which gives an intermediate amount of information result in a fair increase in performance, but less than fully specified correction.}
    \label{tab:ambig}
\end{table}

We also experiment with if our method can generalize to unseen objects in the multi-room domain. We holdout specific objects during training and test on these unseen objects. For example, the agent will not see green triangles but will see other green objects and non-green triangles during training and must generalize to the unseen combination at test time. In Table \ref{tab:holdout}, we see that our method achieves a lower completion rate compared to when specific objects are not held out but is still able to achieve a high completion rate and outperforms the baselines.

\begin{table*}[!htb]
    \centering
    \begin{tabular}{l|cc|cccccc}
    \toprule
         Env  & Full Info & MIVOA (Full Info)& $\lowercase{\mathbf{C}}_0$   &   $\lowercase{\mathbf{C}}_1$   &   $\lowercase{\mathbf{C}}_2$ & $\lowercase{\mathbf{C}}_3$ & $\lowercase{\mathbf{C}}_4$ & $\lowercase{\mathbf{C}}_5$\\
         \hline
         Multi-room  & 0.57    & 0.62        & 0.073  &   0.44 & 0.58 & 0.65 & 0.73 & 0.75 \\

    \bottomrule
    \end{tabular}
    \caption{Experiments on holding out specific objects during training to see if our method can generalize to unseen objects at test time.}
    \vspace{-3mm}
    \label{tab:holdout}
\end{table*}

\subsection{Discussion and Future Work}

We presented meta-learning for guided policies with language (GPL), a framework for interactive learning of tasks with in-the-loop language corrections. In GPL , the policy attempts successive trials in the environment, and receives language corrections that suggest how to improve the next trial over the previous one. The GPL  model is trained via meta-learning, using a dataset of other tasks to learn how to ground language corrections in terms of behaviors and objects. While our method currently uses fake language, future work could incorporate real language at training time. To scale corrections to real-world tasks, it is vital to handle new concepts, in terms of actions or objects, not seen at training time. Approachs to handling these new concepts could be innovations at the model level, such as using meta-learning, or at the interface level, allowing humans to describe new objects to help the agent.

\section*{Acknowledgements}
This work was supported by the Office of Naval Research through a Young Investigator Program Award, Berkeley Deep Drive, the National Science Foundation through IIS-1614653, and computational resources from Amazon and NVIDIA.

redpajama_set_name: RedPajamaArXiv


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Andy Crittenden is the Chief Instructor, Chief Grading Examiner & Head Coach of ACMAC Martial Arts Schools. He is also the Founder of Revolution Martial Arts and is a Senior Member of WKKC England. He has been training since 1987 and hold Black Belts or Instructor Qualifications in a variety of Styles including a 6th Degree Black Belt in Multi Skilled Martial Arts.
Andy started teaching in 1996. He opened ACMAC in 2003 and then in 2010 started the expansion from just the Full Time Centre in Balby to opening Schools around Doncaster which now stands at 4 Full Time Schools. Andy was inducted into the Hall of Fame in 2012 and was awarded Uk Instructor of the Year in 1999, 2000 & 2013.
Audrey is the Senior Head Instructor of ACMAC Martial Arts Schools and the Head Coach of the Patterns & Forms Competition Team, She is also a Grading Examiner. Mrs P has been training for over 25 years and holds a 6th Degree Black Belt in Tae Kwon Do, 4th Degree Black Belt in MSMA, 2nd Degree Black Belt in Weapons as well as being a 1st Degree Black Belt in Kenpo Karate.
Mrs Parker is a Senior Patterns & Forms Judge of the Revolution Tournaments and is a Nationally respected Judge, Instructor and Competitor. She is a National Champion in TKD Patterns & Sport Sword. Mrs P has taught with Mr C since the mid 1990's at Kim Stones Academy and in particular in their Academy at Woodfield Primary School which produced over 20 Black Belts as an after school activity.
Mrs P was inducted into the MAI National Martial Arts Hall of Fame in 2013 as a Top Instructor.
Lucy is a 4th Degree Black Belt in Kick Boxing & The Superfoot System under Bill Wallace and is 2nd Dan in MSMA.
She is also a Level 5 Self Defence Instructor. She has trained with some of the best around including Dave Turton, Bill "Superfoot" Wallace, Joe Tierney, Raymond Daniels, Drew Neal, Joe Lewis, Jeff Smith, Kim Stones, Stevie Smith & Phil Glover.
Lucy is an avid competitor and is the 2013 World & European Champion and Multi Time National Champion. Lucy won ACMAC Student of the Year 2010 & Instructor of the Year in 2012. Lucy won 2 more World Titles in 2017 at the World Championships in Orlando, and another in Ireland in 2018.
Lucy was inducted into the MAI National Martial Arts Hall of Fame in 2015.
Hayden started training with Andy Crittenden and Master Kim Stones at the age of 5 years old. He has been involved in the Martial Arts Industry from an early age as his Mum was a full time employee of Kim Stones Academy and his dad was an Instructor there. Hayden is a Professional Instructor in the ACMAC group teaching both at the HQ in Balby and his own students at the Bentley Academy.
Hayden is a 5th Degree Black Belt in Tae Kwon Do, 4th Degree Black Belt in Kick Boxing & MSMA as well as a Level 4 Self Defence Instructor with the SDF.
He is a Natural Competitor and has represented his Country at the WTKA, ICO & WKC World Championships on 10 occasions. He is multiple time World, International & European Champion and has won over 40 National Titles in Points Fighting, Continuous and as a child Patterns. His most recent win coming in Ireland in 2018 when he became WKC World Champion.
Hayden was inducted into the MAI National Martial Arts Hall of Fame in 2014 & 2015.
Jessica started training with Andy Crittenden and Audrey Parker at their Kim Stones School at Woodfield Primary in 1997 and was one of our first 3 students to join.
Jessica won ACMAC's Competitor of the Year Award in 2006 and Student of the Year Award in 2007 & 2008. Jessica's main Discipline is Tae Kwon Do and she is a 5th Degree Black Belt. Jessica also holds Dan Grades in the Superfoot System, Multi Skilled Martial Arts and Kick Boxing. a Level 4 Self Defence Instructor with the SDF.
In 2012 Jess won the WTKA World & European Championship in Tae Kwon Do Patterns & Weapon Combat. She is an Multi time National Champion.
Jess was inducted into the MAI National Martial Arts Hall of Fame in 2014.
Jordan has been training in the Martial Arts from an early age starting out like most of the Instructor's at Kim Stones Academy with Andy Crittenden and holds 4th Dan in Kick Boxing, 2nd Dan in Ju-Jitsu, Tae Kwon Do and Multi Skilled Martial Arts, he was awarded a Blue Belt in Brazilian Jiu-Jitsu from Royce Gracie and is a Level 1 Self Defence Instructor with ASMAA-SDF.
Jordan has also competed in Mixed Martial Arts, Grappling, Brazilian Jiu-Jitsu, Point Fighting & Light Continuous and won National Titles in Submission Grappling. He has trained with the likes of Billy Robinson, Dave Kovar, Chuck Liddell, Royce Gracie, Dave Turton, Trevor Roberts, Johnny Huskey & Marcelo Brigadeiro.
Jordan was inducted into the MAI National Martial Arts Hall of Fame in 2015 and won ACMAC Instructor of the Year in 2016. He is now responsible for the Cycle Training at ACMAC Balby.
Kelly is a 3rd Degree Black Belt in Kick Boxing who started after watching her son Cieran for a couple of years.
Kelly is a great asset to our team, she has taught all classes Little Dragons, Beginner & Advanced Kids and Adult Kick Boxing classes when the regular Instructor hasnt been there and she has always done a great job. She has a great raport with the students young and old.
She is a Fully Qualifoed Instructor. Kelly is also an avid Competitor and is a Yorkshire Champion and fought for the English Title at the 2012 Revolution Fight Night. Kelly won 3 Bronze Medals at the 2016 WKC World Championships in Dublin, a year later in Florida she won Gold (Points Team) & Silver (Continuous), in 2018 Kelly once again struck Gold in the Women's Team.
Jon is a very well rounded Martial Artist. He is a 3rd Dan Black Belt in Karate and runs Rawcon Karate Club.
He is one of a handful of people to have achieved his 3rd Dan Black Belt in SDF Modern Street Combat and 2nd Dan Kick Boxing too. He achieved his 1st, 2nd & 3rd Dan under Dave Turton 10th Dan. He is the main person who teaches SDF classes when Andy Crittenden is away.
Jon represented his Country at the 2018 World Championships in Ireland bringing home Bronze in both Points & Continuous.
Stefan is one of the most well rounded Martial Artists at ACMAC and has trained with everyone who has ever visited, he is a 2nd Dan Black Belt under Andy Crittenden in MSMA & 2nd Dan SDF Modern Street Combat under Dave Turton and a Certified Catch Wrestler under Billy Robinson.
Stefan lives and breathes Martial Arts and has trained with everyone who has been at ACMAC since he started, he accompanies Andy Crittenden on alot of his seminars around the UK.
Stefan is the UK rep for Danny Hoskins in Glima which is Viking Wrestling & Combat Art and also Irish Stick Fighting.
Mick is a role model Student at ACMAC and holds 2nd Dan Black Belt in both Multi Skilled Martial Arts & SDF Modern Street Combat. He has many years of experience on the doors of Doncaster which comes in handy in both those systems especially SDF.
He is a hard working student who always gives 100% and is well rounded in Self Defence, Weapons, Kick Boxing & Grappling.
Mick teaches a Combat Fitness class every week at ACMAC Balby.
He has competed in many Tournaments in a variety of Disciplies including Gold's in Revolution in Point Fighting. He has also competed in Weapon Forms, Patterns & Sport Sword and has represented his Country on two occassions at the World Championships.
Tiff has been training for 10 years, starting in Little Dragons and then moving on into the Kids System, before moving into the Adults class. She is now a 3rd Dan in Multi Skilled Martial Arts and highly graded in Kick Boxing.
Tiff is a 11x World Champion winning Gold at WKKC, IAKSA & WTKA World Championships. She is also a Multi Time European, International & National Champion.
Kieron has been training at ACMAC for over 9 years and is a 2nd Degree Black Belt in Multi Skilled Martial Arts and a Cycle Belt in Kick Boxing.
He is a good all round Martial Artist winning many Tournaments in Points, Continuous, Forms, Weapon Forms & Weapon Fighting including Silver Medals at the 2014 & 2015 World Championships and in 2017 realised his dream and became World Champion in Continuous Fighting & Team Event, he took Gold & Silver in 2018 too.
Kieron has completed the Instructor Program.
Dom has been training at ACMAC for over 5 years but in Martial Arts much longer and is Black Belt in Freestyle Martial Arts. He is a 1st Dan in Multi Skilled Martial Arts and in Kick Boxing.
He is a good all round Martial Artist winning many Tournaments in Points, Continuous, Forms, Weapon Forms & Weapon Fighting. In 2015 he Won Gold at the European Championships and in 2016 won Silver in Fighting and Bronze in Forms at the World Championships in Ireland. In 2016 Dom won the prestigious Top Student Award.
Dom has completed the Instructor Program.
Dean has trained with Andy Crittenden for over 20 years, he is a 3rd Dan Black Belt in Tae Kwon Do & Brown Belt in Kick Boxing. He has also studied other arts.
Dean was one of the first ACMAC Members when we opened in 2003 and recently came back and got back involved in all aspects of what we do.
Dean always competed and had great success, in 2018 he got straight back into it and has won Medals in both Points & Continuous, he travelled with the England Squad and was a Referee at the World Championships, he is hoping to be a Competitor by the 2019 Worlds.
Sam has been training at ACMAC for over 10 years and is a 2nd Degree Black Belt in Multi Skilled Martial Arts and 1st Degree Black Belt in Tae Kwon Do.
She won a Gold Medal in Tae Kwon Do Patterns at the 2014 World Championships in Italy. In addition to that she has won Gold, Silver & Bronze Medals at numerous National & Regional Championships around the UK.
Sam has completed the Instructor Program.
James is the main Assistant at ACMAC Bentley, he was one of the very first Students and one of the first 2 Black Belts.
James is a 2nd Degree Black Belt in MSMA & Brown Belt in Kick Boxing.
James has competed in a variety of disciplines at Local, National & International Tournaments. in 2017 he represented his country in Orlando at the World Championships competing in Creative Forms & Weapon Forms.
James has completed the Instructor Program.
Jade has been training at ACMAC Balby for the past 5 years but has been doing Martial Arts for most of her life. Jaxe is a 7 time World Champion and one of our most succesful Competitors. She is also a Multi Time European & National Champion and has lots of success on Team events both in Women's Teams plus Mixed Teams where she was the sole female Fighter.
Jade is currently studying to be a PE Teacher after completing her University Degree in the Summer of 2018. Once qualified we are hoping she will open an ACMAC School in her local area.
Jade has completed the Instructor program.
Ange is a 1st Dan in Kick Boxing & 1st Kyu in SDF Modern Street Combat, she started after her daughter Brooke joined our Bentley Academy and has never looked back.
She is one of the main Assistants at ACMAC Bentley and is involved in both Kids & Adults classes.
Ange is a great role model to all female students and always gives 100%.
Lorna is a 1st Dan Black Belt in Kick Boxing and one of the first students at ACMAC Bentley.
Lorna is one of the nicest people you would wish to meet and a great asset to the Team.
Alex has been training at ACMAC for over 7 years and is a 2nd Dan Black Belt, she completed the Instructor course in 2018 and is a great help in classes.
Max has been training at ACMAC for over 7 years and is a 2nd Dan Black Belt, she completed the Instructor course in 2018 and is a great help in classes.
Max has won numerous Championships in Forms, Weapons, Points & Continuous including 2x European Titles, multiple National Titles and also 2x Bronze Medals at the 2018 World Championships.
Josh has been training at ACMAC for over 7 years and is a 2nd Dan Black Belt, she completed the Instructor course in 2018 and is a great help in classes.
Dawid has been training at ACMAC for over 6 years and is a 2nd Dan Black Belt, she completed the Instructor course in 2018 and is a great help in classes.
Aidan has been training at ACMAC for around 8 years but in total for 13 years, he holds a 3rd Dan Black Belt in Freestyle Martial Arts from before he joined us and is 1st Dan in Kick Boxing with us.
Aidan has won World Titles in Team Point Fighting & Continuous Kick Boxing as well as numerous other Championships at home as well as winning medals in Italy, Ireland, USA & Canada.
Oliver completed the Instructor course in 2018 and is a great help at ACMAC Stainforth when he is in class. He is a 1st Dan Black Belt and also graded in Kick Boxing.
Megan completed the Instructor course in 2018 and is a great help at ACMAC Stainforth when she is in class. She is a 1st Dan Black Belt and also graded in Kick Boxing.
Nieve has been training since she was 5 and in 2019 will be taking her Black Belt in Tae Kwon Do. Nieve completed the Instructor course in 2018.
We also have many Instructor's who are still members who for whatever reason are not actively teaching right now.
Craig Oxley & Kev Miller are considered founding Instructor;s at ACMAC without them over the first 10 years especially we would not have been able to achieve the success we did. Both are 4th Dan's but with many more years of experience than most 5th & 6th Dan's.
Sunny Singh (3rd Dan), Nat Loy (3rd Dan), David Colley (3rd Dan) & Stuart Wade (5th Dan) all ran ACMAC Schools at one point in time, all 4 of these were awesome Martial Artists and all won Gold or Siver at World Level.
We are very lucky to have had many other Instructor's who have contributed to our success over the years.
Darren runs Inspire Shirebrook & Clowne Clubs. He came to ACMAC around 5 years ago with other members of his Club and Inspire has become such a big part of ACMAC Schools.
Darren is a great competitor and over an 18 months competed in the WKC World Championships, WTKA World Championships and ISKA US Open, winning Gold in the Mens Team Events taking his World Title Tally to 6. He has also won numerous Silver & Bronze Medals.
Darren is also a Senior Referee for Revolution Tournaments and the Event Manager for the Yorkshire Championships. in 2015 he represented WKKC England as a Point Fighting Referee & Light Continuous judge at the World Championships in Orlando.
Lee runs Inspire Goole Club. He came to ACMAC around 5 years ago with other members of his Club and Inspire has become such a big part of ACMAC Schools.
Lee is a great competitor and has competed in the WKC World Championships and ISKA US Open, winning Gold in the US Open and Silver at rhe WKC Worlds in 2018.
He has also won many UK Tournaments including winning the WKKC National Title in 2015.
Lee is also a Referee & Kata Judge for Revolution Tournaments.
Sarah runs Inspire Worksop Club. She came to ACMAC around 5 years ago with other members of his Club and Inspire has become such a big part of ACMAC Schools.
Sarah has won many competitions before coming to ACMAC As well as over the last couple of years since training with us. She has won a World Title and numerous other accolades.
She has Refereed & Judged most disciplines at Revolution.
Joanne is an Instructor at Inspire Shirebrook & Clowne Clubs.
She has also won numerous Gold, Silver & Bronze Medals at domestic events in Kata, Weapon Forms, Point Fighting and Sport Sword.
Joanne is also a Kata Judge for Revolution Tournaments and one of the Event Managers for the Yorkshire Championships.
Beth came to ACMAC around 5 years ago with other members of her Club and Inspire has become such a big part of ACMAC Schools.
She is a great competitor and over qn 18 month period competed in the WKC World Championships, WTKA World Championships and ISKA US Open, winning Gold in the US Open, WTKA Worlds & Silver at the WKC Worlds.
Beth has also been very succesful in the UK winning numerous European & National Titles as well as fighting on the ACMAC Ladies Team on both Kwon Team Title wins, she is also a Revolution Grand Champion.
Beth is a Referee for Revolution Tournaments. She is currently a Member of the Police Force.
Lee is one of the main Instructor's at Inspire Shirebrook & Clowne. He is an avid competitor along with his Wife Caroline and children Jessica & Daniel. All 4 of them are Black Belts.
Alan is an Inspire Member and assisted at ACMAC Balby for many years too.
Alan is a Good competitor and won the ISKA US Open World Title & WTKA European Title this year in Kata. He is a retired School Teacher so his experience in Mainstream Education is a great addition to the Team.
Our Instructor's are some of the finest in the Country. All of them are highly trained Fully Qualified Black Belt Instructor's. The individuals listed below are responsible for all classes at ACMAC Balby, Bentley, Rossington & Stainforth Academies as well as all Lessons taught in the various Primary & Secondary Schools we teach in throughout Doncaster.
Our Instructor's are DBS Checked, Insured and First Aider's and are members of Alliance Martial Arts & Self Defence Association & some are part of WKKC England & ASMAA-SDF too.
All ACMAC Instructor's are Black Belts and most are Multi-Disciplined Black Belts, all have started out in Tae Kwon Do or Kick Boxing and are now studying Multi Skilled Martial Arts either working towards there Black Belts or they have already achieved it. As well as their Martial Arts qualifications some of our Instructors are also qualified to teach Self Defence & Fitness. Many of Instructor's are also current or former World, European, International. British & English Champions in Point Fighting, Light Continuous, Tae Kwon Do Patterns, Freestyle Forms, Musical Forms, Weapon Forms, Sport Sword, Weapon Fighting and Submission Wrestling.
Above all else our Instructor's are dedicated Martial Artists hand picked by Chief Instructor Andy Crittenden to train as an Instructors and have gone through not only years of training in the Martial Arts but years of Instructor Training too. The ACMAC Instructor's continue to train and learn from Chief Instructor Andy Crittenden and some of the World Class Instructor's we bring in on seminars like Dave Turton, Kim Stones, Dave Kovar, Bill "Superfoot" Wallace, Joe Tierney, Royce Gracie, Danny Hoskins, Joe Lewis, Raymond Daniels, Jack Felton, Robbie Lavoie, Rory Miller, Wade Schalles, Johnny Huskey, Jeff Smith, Drew Neal, Mark Wiley, Marty Jones, Roland Osborne, Tom Callos, Stevie Smith and the late great Billy Robinson.
Our Instructor's goals are to help you achieve yours!


Loving Krishna means loving everyone because Krsna is in their heart. Conversely, applying that logic backwards, if you don't love everyone, it means that you are not loving Krsna. Using the root and leaves example, if the leaves are looking healthy, it is to be understood that the root is receiving nourishment.
In SB, eleventh canto, it is explained that only the kanishta or neophyte sees Krsna in the temple, but not in the hearts of all, and fails to respect and develop loving relationships with them. In my experience, the leaves and root example has been used in our preaching to discourage against trying to develop loving relationships that are devoid of a sense of everyone's natural attachment to the tree of Krsna, who alone can fulfill of their insatiable need for happiness forever.
But there is an important consideration which needs to be taken into account. While it is true that while ignoring the root, the leaves will suffer, one must keep in mind also the limitation of the tree example. Unlike a tree, the root is within the heart of the very leaf itself. It is not that by offering respect to Krsna in the temple, at the same time ignoring His presence in the hearts of all living entities, somehow all living entities will be satisfied. Why? Because that is not bhakti. Lord Kapiladeva in 3rd Canto SB says that such attempted worship is like pouring ghee into ashes- utterly ineffective.
Pouring water on the root means to actually offer love, otherwise one is pouring something else. Only water will work, not oil, molten lead or some other liquid. These will cause the leaves to wither. Kanishta adhikari worship of the Deity in the temple, is like pouring molten lead on the root of the tree, or perhaps old cooking oil. It is old because it has been used to cook our material desires- for exploitation of other living entities for self-aggrandizement (material).
If we are pouring actual water, or giving our bhakti to Krsna, then simultaneously, and by the exact same increments, we should see our love for every one of his parts and parcels grow. Thus, one's heart expands. It has to- Krsna is bigger than the biggest! The universes reside within His form. Loving Him means your heart explodes with love for the whole creation.
Thus a devotee develops friendly loving relationships with all living entities.
Niscala Dasi writes in the article, "Kanishta adhikari worship of the Deity in the temple, is like pouring molten lead on the root of the tree, or perhaps old cooking oil." This is a nonsensical analogy. Kanistha adhikari, or third-class devotion, is not pernicious. On the contrary, it is beneficial and purifying, and can eventually lead to second-class devotion.


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Charlie's Colorforms City Season 2: Premiere Date, Cast, Recap, Update
Anisha Dutta
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When you are a parent, sometimes, you do want your kid(s) to sit still and stay quiet. It is necessary, we understand, both for your sanity and your toddler's benefit. But how do you do that? With a little bit of help, right? And what if that help comes in the form of an online babysitter — through a program that makes sure, your toddler will stay absorbed at least for a few hours a day. And when you speak of children's TV shows, it is also necessary that they are informative along with being enjoyable. After all, these years are pretty influential for your kiddo's growth. Well, no worries. Netflix is there to the rescue, with a huge repository of interesting, educational, and fun-filled kid's movies and shows. And to add to its catalog, there's yet another series that teaches only good things to your kid — teasing his/her little brain and compelling him/her to think. So, what is it? Let's read about it in detail.
Charlie's Colorforms City is an animated preschool show that first premiered on the global streaming giant Netflix on March 22, 2019. The show is based on the hit and classic toy line — Colorforms — from 9 Story-owned Out of the Blue Enterprises. This story-telling adventure series is based on the protagonist as he goes on unpredictable, exciting, and imaginative shape-filled expeditions. Charlie's Colorforms City features Out of the Blue co-founders Angela Santomero and Samantha Freeman as the executive producers alongside DHX Media, Canada.
Charlie's Colorforms City Cast: Who is in it?
Charlie's Colorforms City features Jacob Soley, Saara Chaudry, and Tyler Barish as the voiceover artists.
Actor Jacob Soley is best known for his performances in Flatliners, American Gothic, and Kids On. ACTRA Award winner and Canadian actress Saara Chaudry is famous for her voiceovers in The Breadwinner and for being one of the finalists in Voice Acting in a Feature Production at the 45th Annie Awards. She has also featured in The Curse of Clara: A Holiday Tale, Combat Hospital, Degrassi, Odd Squad, Max & Shred, and Dino Dana. British actor Tyler Barish is well-known for his roles in Anne, Reign, and The Magic Lantern.
Charlie's Colorforms City Plot: What is it about?
Charlie's Colorforms City is based on the famous toy brand Colorforms. Colorforms are basically cute, simple toys that are created out of colorful vinyl sheets and are able to stick on a smooth surface, without using any adhesives. The various movable forms (parts) of the toys can be removed and repositioned to create numerous, different types of figures. What started as simple geometric shapes of triangles, circles, and squares, eventually progressed to include full-colored illustrated play sets, games, puzzles, interactive books, and creative activity sets for children of all ages.
In Charlie's Colorforms City, Charlie takes viewers on a sticky, innovative storytelling adventure, where he creates cute, creative shapes on the way. The magical, loveable and hilarious Charlie is accompanied with a colorful cast of characters during his quests. Along with this playful team, your kiddo can travel to his favorite virtual places — to outer space, to the Wild West, or stay right in his home — wherever he wants to go!
Charlie's Colorforms City Season 2 Release Date: When will it premiere?
Charlie's Colorforms City season 1 premiered on Netflix on March 22, 2019. This show, with its fun-filled, safe, and imaginative structure, is a wonderful, educational program for kids.
Considering the fact that this is an animated show, its chances of renewal are very high. Usually networks and studios build IT infrastructure for animated shows and instead of throwing that away, they can very well use it to create another season of the show. That's why we believe that the announcement for Season 2 is just matter of time. In fact we feel Charlie Colorforms City Season 2 can release sometime in March, 2020.
Charlie's Colorforms City Trailer
Check out the season 1 trailer and all previous episodes of the show right here.
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Steven Kirby
High School: Overbrook High School
Street: Markoe Street
Rank: CPL
Date of Casualty: 24-Feb-1967
Province of Casualty: Tay NInh
Daring and independent, Kirby joined the Army when he was 13 years old. He was honorably discharged several months later when his age was discovered, but the youngster remained thoroughly convinced the Army was for him. Kirby returned to Overbrook High School and became a star football player. He reenlisted in early 1967, and this time stayed in the Army. The corporal, a rifleman, was sent to Vietnam, where he was assigned to Company A of the 1st Battalion, 16th Infantry, 1st infantry Division. Kirby died on Feb. 24, 1967, two days after his 19th birthday. He was survived by his mother, three brothers and two sisters.