Posterior Sampling: Make Reinforcement Learning Sample Efficient Again
Machine learning thrives on leveraging structure in data, and many breakthroughs (e.g. convolutional networks) have been made by designing algorithms which exploit the underlying structure of a distribution. Reinforcement Learning agents interact with worlds that are similarly full of structure. For example, no sequence of actions an agent takes will ever cause the laws of physics to change, therefore an agent which learns to generalize such laws through time and space will have an advantage. Sample efficient reinforcement learning can be accomplished when assuming that the world has structure and designing learning algorithms which exploit this assumption without knowing the actual structure beforehand. Posterior Sampling for Reinforcement Learning (PSRL) citep{strens2000bayesian} is such a method which assumes structure in the world and exploits it for learning. A PSLR learning agent first samples models of the environment which conform to both prior assumptions on the world*s structure and past observations and then interacts with the true environment using a policy guided by the sampled model of the environment. While PSRL delivers theoretical Bayesian regret bounds, there are many open issues which must be addressed before PSRL can be applied to current benchmark continuous reinforcement reinforcement tasks. In this work, we identify these issues and find practical solutions to them leading to a novel algorithm we call Neural-PSRL. We validate the algorithm*s effectiveness by achieving state of the art results in the HalfCheetah-v3 and Hopper-v3 domains.
Posterior Sampling: Make Reinforcement Learning Sample Efficient Again =============================================================== This paper proposes an implementation of PSRL via Neural-PSRL: a neural network is trained fit the transition dynamics, with a last-layer linear approximation used to give a posterior distribution. This model can be used in conjunction with a policy gradient algorithm to give an exploratory policy. Evaluated on Mujoco benchmarks this leads to state of the art performance. There are several things to like about this paper: - The problem of extending *efficient exploration* to Deep RL is an important one, and PSRL seems like one of the most suitable general approaches to base an algorithm on. - The proposed algorithm seems reasonable, and seems like it also performs well. - The approach to writing and accessible code release should be praised. However, there are some other places where this paper falls down: - Despite the focus on *incorporating prior knowledge* in the early part of the paper, it seems like the proposed method actually incorporates very little prior knowledge... for example, the *Eluder dimension* of the space represented by these neural networks is presumably huge! This seems like a bit of a bait and switch. - I don*t think the experiments offer a huge amount of *insight* into what is really happening in the algorithm. It would be helpful to show *really good* performance even on a toy domain, so that you can really say that the exploration via PSRL is really working... maybe something like bsuite = https://github.com/deepmind/bsuite and particularly the *deep sea* experiments could help? - It*s a problem that the actual main paper doesn*t give a clear and concise description of the algorithm used. I think this is quite important and it would be much better to put the older results on PSRL to the appendix instead. Minor points: - I*m not sure the title is a great idea... it might put off some people, but also I don*t think it*s really warranted from the results... it seems like Neural-PSRL is a potentially interesting avenue, but hardly making anything great (again?)! - You might want to connect / contrast this work vs the line of research around *Randomized Value Functions* http://jmlr.org/papers/v20/18-339.html... which is another approach that tries to extend something like PSRL to Deep RL. Overall, I do think this is an interesting paper, and a promising avenue for research. However there are probably too many places where the paper could be tightened up: clarity of algorithm description, clarity of support/results, separation of actual claims vs claims of *making great* ;D.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Posterior Sampling: Make Reinforcement Learning Sample Efficient Again
Machine learning thrives on leveraging structure in data, and many breakthroughs (e.g. convolutional networks) have been made by designing algorithms which exploit the underlying structure of a distribution. Reinforcement Learning agents interact with worlds that are similarly full of structure. For example, no sequence of actions an agent takes will ever cause the laws of physics to change, therefore an agent which learns to generalize such laws through time and space will have an advantage. Sample efficient reinforcement learning can be accomplished when assuming that the world has structure and designing learning algorithms which exploit this assumption without knowing the actual structure beforehand. Posterior Sampling for Reinforcement Learning (PSRL) citep{strens2000bayesian} is such a method which assumes structure in the world and exploits it for learning. A PSLR learning agent first samples models of the environment which conform to both prior assumptions on the world*s structure and past observations and then interacts with the true environment using a policy guided by the sampled model of the environment. While PSRL delivers theoretical Bayesian regret bounds, there are many open issues which must be addressed before PSRL can be applied to current benchmark continuous reinforcement reinforcement tasks. In this work, we identify these issues and find practical solutions to them leading to a novel algorithm we call Neural-PSRL. We validate the algorithm*s effectiveness by achieving state of the art results in the HalfCheetah-v3 and Hopper-v3 domains.
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>



Posterior Sampling: Make Reinforcement Learning Sample Efficient Again =============================================================== This paper proposes an implementation of PSRL via Neural-PSRL: a neural network is trained fit the transition dynamics, with a last-layer linear approximation used to give a posterior distribution. This model can be used in conjunction with a policy gradient algorithm to give an exploratory policy. Evaluated on Mujoco benchmarks this leads to state of the art performance. There are several things to like about this paper: - The problem of extending *efficient exploration* to Deep RL is an important one, and PSRL seems like one of the most suitable general approaches to base an algorithm on. - The proposed algorithm seems reasonable, and seems like it also performs well. - The approach to writing and accessible code release should be praised. However, there are some other places where this paper falls down: - Despite the focus on *incorporating prior knowledge* in the early part of the paper, it seems like the proposed method actually incorporates very little prior knowledge... for example, the *Eluder dimension* of the space represented by these neural networks is presumably huge! This seems like a bit of a bait and switch. - I don*t think the experiments offer a huge amount of *insight* into what is really happening in the algorithm. It would be helpful to show *really good* performance even on a toy domain, so that you can really say that the exploration via PSRL is really working... maybe something like bsuite = https://github.com/deepmind/bsuite and particularly the *deep sea* experiments could help? - It*s a problem that the actual main paper doesn*t give a clear and concise description of the algorithm used. I think this is quite important and it would be much better to put the older results on PSRL to the appendix instead. Minor points: - I*m not sure the title is a great idea... it might put off some people, but also I don*t think it*s really warranted from the results... it seems like Neural-PSRL is a potentially interesting avenue, but hardly making anything great (again?)! - You might want to connect / contrast this work vs the line of research around *Randomized Value Functions* http://jmlr.org/papers/v20/18-339.html... which is another approach that tries to extend something like PSRL to Deep RL. Overall, I do think this is an interesting paper, and a promising avenue for research. However there are probably too many places where the paper could be tightened up: clarity of algorithm description, clarity of support/results, separation of actual claims vs claims of *making great* ;D.
assistant
Posterior Sampling: Make Reinforcement Learning Sample Efficient Again =============================================================== This paper proposes an implementation of PSRL via Neural-PSRL: a neural network is trained fit the transition dynamics, with a last-layer linear approximation used to give a posterior distribution. This model can be used in conjunction with a policy gradient algorithm to give an exploratory policy. Evaluated on Mujoco benchmarks this leads to state of the art performance. There are several things to like about this paper: - The problem of extending *efficient exploration* to Deep RL is an important one, and PSRL seems like one of the most suitable general approaches to base an algorithm on. - The proposed algorithm seems reasonable, and seems like it also performs well. - The approach to writing and accessible code release should be praised. However, there are some other places where this paper falls down: - Despite the focus on *incorporating prior knowledge* in the early part of the paper, it seems like the proposed method actually incorporates very little prior knowledge... for example, the *Eluder dimension* of the space represented by these neural networks is presumably huge! This seems like a bit of a bait and switch. - I don*t think the experiments offer a huge amount of *insight* into what is really happening in the algorithm. It would be helpful to show *really good* performance even on a toy domain, so that you can really say that the exploration via PSRL is really working... maybe something like bsuite = https://github.com/deepmind/bsuite and particularly the *deep sea* experiments could help? - It*s a problem that the actual main paper doesn*t give a clear and concise description of the algorithm used. I think this is quite important and it would be much better to put the older results on PSRL to the appendix instead. Minor points: - I*m not sure the title is a great idea... it might put off some people, but also I don*t think it*s really warranted from the results... it seems like Neural-PSRL is a potentially interesting avenue, but hardly making anything great (again?)! - You might want to connect / contrast this work vs the line of research around *Randomized Value Functions* http://jmlr.org/papers/v20/18-339.html... which is another approach that tries to extend something like PSRL to Deep RL. Overall, I do think this is an interesting paper, and a promising avenue for research. However there are probably too many places where the paper could be tightened up: clarity of algorithm description, clarity of support/results, separation of actual claims vs claims of *making great* ;D.