Learning World Graph Decompositions To Accelerate Reinforcement Learning
Efficiently learning to solve tasks in complex environments is a key challenge for reinforcement learning (RL) agents. We propose to decompose a complex environment using a task-agnostic world graphs, an abstraction that accelerates learning by enabling agents to focus exploration on a subspace of the environment.The nodes of a world graph are important waypoint states and edges represent feasible traversals between them. Our framework has two learning phases: 1) identifying world graph nodes and edges by training a binary recurrent variational auto-encoder (VAE) on trajectory data and 2) a hierarchical RL framework that leverages structural and connectivity knowledge from the learned world graph to bias exploration towards task-relevant waypoints and regions. We show that our approach significantly accelerates RL on a suite of challenging 2D grid world tasks: compared to baselines, world graph integration doubles achieved rewards on simpler tasks, e.g. MultiGoal, and manages to solve more challenging tasks, e.g. Door-Key, where baselines fail.
Summary: This paper proposes an approach to identifying important waypoint states in RL domains in an unsupervised fashion, and then for using these states within a hierarchical RL approach. Specifically, the authors propose to use a binary latent variable VAE to identify waypoint states, then an HRL algorithm uses these waypoints as intermediate goals to better decompose large RL domains. The authors show that on several grid world tasks, the resulting policies substantially outperform baseline approaches. Comments: I have mixed opinions on this paper, though ultimately feel that it is below the bar for acceptance. There are a lot of aspects to the proposed approach, and overall I*m left with the impression that there are some interesting and worthwhile aspects to the proposed method, but ultimately it is hard to disentangle precisely what is the effect of each contribution. For example, let*s consider the waypoint discovery method. The basic approach is to use a binary latent variable VAE, using a recently proposed Hard Kumaraswamy distribution to model the latent state. This seems like a reasonable approach, but there*s no real analysis of the actual waypoints that are discovered in the target domains, whether they indeed correspond to intuitively important waypoints in a domain, or whether they are just producing some arbitrary segmentation of the proposed task. The other elements of the paper have similar issues for me. The whole HRL process, using these waypoint states as intermediate goals, seems reasonable, but it*s hard to disentangle the performance of this particular approach versus the performance of any approach that would use (any) intermediate states as goals within an HRL approach. And the impression I*m left with, given the level of detail included I the paper, is that I would have no idea how to apply or extend this process to any other RL domains. I looked at the provided code hoping it would help to clarify some of the implementation details, but the code is not at all a complete collection of routines that could re-create the experiments. Rather, the code just includes a few of the model architectures, which aren*t really the important aspects of this work. Thus, I*m overall left with the impression that it*s quite difficult to assess the contribution of this approach, and determine precisely which of the different proposed aspects is really contributing most to the improved performance. I know there is some ablative analysis in the paper comparing the pi_g-init and G_w-traversal independently and together, but I*m more questioning the basic question of what each portion of the network is really learning. I*d be curious if the authors are able to clarify any of these points during their rebuttal.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Learning World Graph Decompositions To Accelerate Reinforcement Learning
Efficiently learning to solve tasks in complex environments is a key challenge for reinforcement learning (RL) agents. We propose to decompose a complex environment using a task-agnostic world graphs, an abstraction that accelerates learning by enabling agents to focus exploration on a subspace of the environment.The nodes of a world graph are important waypoint states and edges represent feasible traversals between them. Our framework has two learning phases: 1) identifying world graph nodes and edges by training a binary recurrent variational auto-encoder (VAE) on trajectory data and 2) a hierarchical RL framework that leverages structural and connectivity knowledge from the learned world graph to bias exploration towards task-relevant waypoints and regions. We show that our approach significantly accelerates RL on a suite of challenging 2D grid world tasks: compared to baselines, world graph integration doubles achieved rewards on simpler tasks, e.g. MultiGoal, and manages to solve more challenging tasks, e.g. Door-Key, where baselines fail.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



Summary: This paper proposes an approach to identifying important waypoint states in RL domains in an unsupervised fashion, and then for using these states within a hierarchical RL approach. Specifically, the authors propose to use a binary latent variable VAE to identify waypoint states, then an HRL algorithm uses these waypoints as intermediate goals to better decompose large RL domains. The authors show that on several grid world tasks, the resulting policies substantially outperform baseline approaches. Comments: I have mixed opinions on this paper, though ultimately feel that it is below the bar for acceptance. There are a lot of aspects to the proposed approach, and overall I*m left with the impression that there are some interesting and worthwhile aspects to the proposed method, but ultimately it is hard to disentangle precisely what is the effect of each contribution. For example, let*s consider the waypoint discovery method. The basic approach is to use a binary latent variable VAE, using a recently proposed Hard Kumaraswamy distribution to model the latent state. This seems like a reasonable approach, but there*s no real analysis of the actual waypoints that are discovered in the target domains, whether they indeed correspond to intuitively important waypoints in a domain, or whether they are just producing some arbitrary segmentation of the proposed task. The other elements of the paper have similar issues for me. The whole HRL process, using these waypoint states as intermediate goals, seems reasonable, but it*s hard to disentangle the performance of this particular approach versus the performance of any approach that would use (any) intermediate states as goals within an HRL approach. And the impression I*m left with, given the level of detail included I the paper, is that I would have no idea how to apply or extend this process to any other RL domains. I looked at the provided code hoping it would help to clarify some of the implementation details, but the code is not at all a complete collection of routines that could re-create the experiments. Rather, the code just includes a few of the model architectures, which aren*t really the important aspects of this work. Thus, I*m overall left with the impression that it*s quite difficult to assess the contribution of this approach, and determine precisely which of the different proposed aspects is really contributing most to the improved performance. I know there is some ablative analysis in the paper comparing the pi_g-init and G_w-traversal independently and together, but I*m more questioning the basic question of what each portion of the network is really learning. I*d be curious if the authors are able to clarify any of these points during their rebuttal.
assistant
Summary: This paper proposes an approach to identifying important waypoint states in RL domains in an unsupervised fashion, and then for using these states within a hierarchical RL approach. Specifically, the authors propose to use a binary latent variable VAE to identify waypoint states, then an HRL algorithm uses these waypoints as intermediate goals to better decompose large RL domains. The authors show that on several grid world tasks, the resulting policies substantially outperform baseline approaches. Comments: I have mixed opinions on this paper, though ultimately feel that it is below the bar for acceptance. There are a lot of aspects to the proposed approach, and overall I*m left with the impression that there are some interesting and worthwhile aspects to the proposed method, but ultimately it is hard to disentangle precisely what is the effect of each contribution. For example, let*s consider the waypoint discovery method. The basic approach is to use a binary latent variable VAE, using a recently proposed Hard Kumaraswamy distribution to model the latent state. This seems like a reasonable approach, but there*s no real analysis of the actual waypoints that are discovered in the target domains, whether they indeed correspond to intuitively important waypoints in a domain, or whether they are just producing some arbitrary segmentation of the proposed task. The other elements of the paper have similar issues for me. The whole HRL process, using these waypoint states as intermediate goals, seems reasonable, but it*s hard to disentangle the performance of this particular approach versus the performance of any approach that would use (any) intermediate states as goals within an HRL approach. And the impression I*m left with, given the level of detail included I the paper, is that I would have no idea how to apply or extend this process to any other RL domains. I looked at the provided code hoping it would help to clarify some of the implementation details, but the code is not at all a complete collection of routines that could re-create the experiments. Rather, the code just includes a few of the model architectures, which aren*t really the important aspects of this work. Thus, I*m overall left with the impression that it*s quite difficult to assess the contribution of this approach, and determine precisely which of the different proposed aspects is really contributing most to the improved performance. I know there is some ablative analysis in the paper comparing the pi_g-init and G_w-traversal independently and together, but I*m more questioning the basic question of what each portion of the network is really learning. I*d be curious if the authors are able to clarify any of these points during their rebuttal.