Answers.Body: <p>For creating a model to predict human-like mouse movements, i think you should try sequential models because they can handle the time-dependent nature of the task as per your requirements</p>
  <p>You should study the following things</p>
  <p><strong>1. Recurrent Neural Networks (RNNs):</strong></p>
  <p><strong>What to learn</strong>: Understand how RNNs can handle sequential data. like in RNN the output of the previous node is fed as input in the current node and it will help to handle the time series data.</p>
  <p><strong>Application</strong>: The current and target positions can be the input, and the RNN can learn the sequential steps required to move from the current to the target position.</p>
  <p><strong>Limitation</strong>: RNNs may struggle with long-term dependencies. like they are good for handling the short-term data</p>
  <p><strong>2. Long Short-Term Memory (LSTM):</strong></p>
  <p><strong>What to learn</strong>: Learn how LSTM networks overcome RNN limitations. They are good at handling long-term dependancies and remember past data for a very long time as compared to the simple RNN model.</p>
  <p><strong>Application</strong>: LSTMs can model long-term dependencies, which could be useful for complex mouse movements with varying lengths.</p>
  <ol start="3">
  <li><strong>Reinforcement Learning (RL):</strong></li>
  </ol>
  <p><strong>What to learn</strong>: Explore reinforcement learning, where the model learns by taking actions (e.g., mouse movements) to maximize a reward (e.g., reaching the target). It like training the small baby to do some task by teaching that or in simple words training your dog to do some task like if he does it well treat him with reward or if he does some thing else teach him to do right things.</p>
  <p><strong>Application</strong>: You could set the reward for successfully reaching the target with smooth, human-like movements.</p>
  <p><strong>Popular Algorithms</strong>: Q-learning, Proximal Policy Optimization (PPO), and Deep Q-Networks (DQN) are common methods in RL for tasks requiring decision-making over time.</p>
  <p>Firstly you should Start with RNNs/LSTMs and experiment with supervised learning to predict intermediate steps between the current and target positions.</p>
  <p>Then, Move on to RL if you want the model to learn through trial and error to optimize its movements over time.</p>
  
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Answers.UserDisplayName: Keval Pandya
Answers.Body: <p><strong>TLDR</strong>: I disagree with the accepted answer. I'd suggest using imitation learning (IL) instead of reinforcement learning (RL) to model and solve this problem.</p>
  <p>Before focusing on which ML model (e.g., RNN or LSTM) to use, I would suggest identifying the class of problem (e.g., supervised learning or RL) you're trying to solve. For example, what are your assumptions about the state and action spaces of the agent? Is the agent's state space continuous or discrete? Are the agent's actions sequential or episodic? One of the early chapters (maybe Ch. 2?) of <a href="https://aima.cs.berkeley.edu/" rel="nofollow noreferrer">Russell and Norvig's textbook</a> discusses these properties using various examples.</p>
  <p>Similarly, if we assume that we're learning some model, what will we learn from? It may prove difficult to write down an explicit reward function for &quot;human-like&quot; mouse movements. But you can probably collect demonstrations: Record the position of your mouse over time when targeting particular <span class="math-container">$(x,y)$</span> pixels, forming a dataset <span class="math-container">$\mathcal{D} = \{ [(x_0, y_0), \dots, (x_n, y_n)] \}$</span>. Hopefully, these demonstrations will capture some notion of what it means to move a mouse like a human.</p>
  <p>Given this dataset, you could use methods for <em>imitation learning</em>, or <em>learning from demonstration</em>, to learn the desired behavior. Unlike RL, these problem settings don't assume a known reward function, and instead use a set of expert demonstrations to represent the target policy.</p>
  <p>Here's a reference providing an introduction to <a href="https://web.stanford.edu/class/cs237b/pdfs/lecture/lecture_10111213.pdf" rel="nofollow noreferrer">Imitation Learning</a>.</p>
  
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Answers.Text: Thanks for this answer, I'm still trying to solve this problem and I'd like to explore any new paths. The closest I got to the desired output was using Transofmers (only encoding).
  
  Since I need this model to imitate my behavirous, the dataset I'm using is record of my own data movements. I made a python program that shows targets on the screen, one at a time, I move the mouse to click the target, then a new one appears, this is the source of dataset.
  
  I also found out that using angle data and distance is better than $[(x_0, y_0), (x_t, y_t)]$
  
  You still think I can do this with IL?
Answers.UserDisplayName: Whicked Hayo
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Answers.UserDisplayName: benned-h
Body: <p>I'm trying to make an ML model that can perform human-like mouse movements, the necessary inputs I want to feed it is the current position and the target position (x_0, y_0) and (x_t, y_t), I need the model to predict all the steps until target.</p>
  <p>My question is I don't know which class of artificial intelligence I should use for this. I saw RNN, LTSM and there's probably more, I just need a direction to follow so I know what I should learn next.</p>
  <p>Thank you!</p>
  
Tags: <neural-networks><machine-learning><time-series>
Title: Training a model to perform mouse movement like a human
UserDisplayName: Whicked Hayo
ThreadText: # Training a model to perform mouse movement like a human
  
  (Asked by: username_0 on 2024-09-18)
  
  I'm trying to make an ML model that can perform human-like mouse movements, the necessary inputs I want to feed it is the current position and the target position (x\_0, y\_0) and (x\_t, y\_t), I need the model to predict all the steps until target.
  
  My question is I don't know which class of artificial intelligence I should use for this. I saw RNN, LTSM and there's probably more, I just need a direction to follow so I know what I should learn next.
  
  Thank you!
  
  **Tags:** neural-networks, machine-learning, time-series
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2024-09-19. Score: 0 | Accepted answer
  
  For creating a model to predict human-like mouse movements, i think you should try sequential models because they can handle the time-dependent nature of the task as per your requirements
  
  You should study the following things
  
  **1. Recurrent Neural Networks (RNNs):**
  
  **What to learn**: Understand how RNNs can handle sequential data. like in RNN the output of the previous node is fed as input in the current node and it will help to handle the time series data.
  
  **Application**: The current and target positions can be the input, and the RNN can learn the sequential steps required to move from the current to the target position.
  
  **Limitation**: RNNs may struggle with long-term dependencies. like they are good for handling the short-term data
  
  **2. Long Short-Term Memory (LSTM):**
  
  **What to learn**: Learn how LSTM networks overcome RNN limitations. They are good at handling long-term dependancies and remember past data for a very long time as compared to the simple RNN model.
  
  **Application**: LSTMs can model long-term dependencies, which could be useful for complex mouse movements with varying lengths.
  
  3. **Reinforcement Learning (RL):**
  
  **What to learn**: Explore reinforcement learning, where the model learns by taking actions (e.g., mouse movements) to maximize a reward (e.g., reaching the target). It like training the small baby to do some task by teaching that or in simple words training your dog to do some task like if he does it well treat him with reward or if he does some thing else teach him to do right things.
  
  **Application**: You could set the reward for successfully reaching the target with smooth, human-like movements.
  
  **Popular Algorithms**: Q-learning, Proximal Policy Optimization (PPO), and Deep Q-Networks (DQN) are common methods in RL for tasks requiring decision-making over time.
  
  Firstly you should Start with RNNs/LSTMs and experiment with supervised learning to predict intermediate steps between the current and target positions.
  
  Then, Move on to RL if you want the model to learn through trial and error to optimize its movements over time.
  
  
  ---
  
  ### Answer by username_2 on 2024-09-30. Score: 0
  
  **TLDR**: I disagree with the accepted answer. I'd suggest using imitation learning (IL) instead of reinforcement learning (RL) to model and solve this problem.
  
  Before focusing on which ML model (e.g., RNN or LSTM) to use, I would suggest identifying the class of problem (e.g., supervised learning or RL) you're trying to solve. For example, what are your assumptions about the state and action spaces of the agent? Is the agent's state space continuous or discrete? Are the agent's actions sequential or episodic? One of the early chapters (maybe Ch. 2?) of [Russell and Norvig's textbook](https://aima.cs.berkeley.edu/) discusses these properties using various examples.
  
  Similarly, if we assume that we're learning some model, what will we learn from? It may prove difficult to write down an explicit reward function for "human-like" mouse movements. But you can probably collect demonstrations: Record the position of your mouse over time when targeting particular $(x,y)$ pixels, forming a dataset $\mathcal{D} = \{ [(x\_0, y\_0), \dots, (x\_n, y\_n)] \}$. Hopefully, these demonstrations will capture some notion of what it means to move a mouse like a human.
  
  Given this dataset, you could use methods for *imitation learning*, or *learning from demonstration*, to learn the desired behavior. Unlike RL, these problem settings don't assume a known reward function, and instead use a set of expert demonstrations to represent the target policy.
  
  Here's a reference providing an introduction to [Imitation Learning](https://web.stanford.edu/class/cs237b/pdfs/lecture/lecture_10111213.pdf).
  
  > **Comments:**
  > **username_0**: Thanks for this answer, I'm still trying to solve this problem and I'd like to explore any new paths. The closest I got to the desired output was using Transofmers (only encoding).  Since I need this model to imitate my behavirous, the dataset I'm using is record of my own data movements. I made a python program that shows targets on the screen, one at a time, I move the mouse to click the target, then a new one appears, this is the source of dataset.  I also found out that using angle data and distance is better than $[(x_0, y_0), (x_t, y_t)]$  You still think I can do this with IL?  
  
  ---
  
  