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.
The paper suggests a practical implementation of Posterior Sampling for Reinforcement Learning (PSLR) using deep networks. The PSLR framework assumes that the model of the environment is sampled from a relatively lower dimensional distribution (for example because of some inherent structure they posses), and exploits this to reduce the sampling complexity to learn an optimal policy, so that the number of samples required is bounded by the complexity of the model distribution, and not by the dimensionality of the state or action space. The paper is well motivated and well written, and the research direction is indeed interesting. I would like to see the implementation section pushing further the limits of the theory by using a more complex posterior model – rather than a simple linear layer – and by testing on more challenging environments, where explicitly exploiting the structure of the non-linear model is essential to solve the task. Regarding the implementation, in Section 4.2 the model is parametrized as s* = W z + s, where W is sampled from the posterior and z is the output of a feed-forward network. Could you further comment on which data the feed-forward network is trained on? Does the theoretical framework remain valid if the feed-forward network is trained only on states from the environment on which the PSLR will later be tested? Wouldn*t that imply that the prior model distribution is already biased toward the particular environment?
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>



The paper suggests a practical implementation of Posterior Sampling for Reinforcement Learning (PSLR) using deep networks. The PSLR framework assumes that the model of the environment is sampled from a relatively lower dimensional distribution (for example because of some inherent structure they posses), and exploits this to reduce the sampling complexity to learn an optimal policy, so that the number of samples required is bounded by the complexity of the model distribution, and not by the dimensionality of the state or action space. The paper is well motivated and well written, and the research direction is indeed interesting. I would like to see the implementation section pushing further the limits of the theory by using a more complex posterior model – rather than a simple linear layer – and by testing on more challenging environments, where explicitly exploiting the structure of the non-linear model is essential to solve the task. Regarding the implementation, in Section 4.2 the model is parametrized as s* = W z + s, where W is sampled from the posterior and z is the output of a feed-forward network. Could you further comment on which data the feed-forward network is trained on? Does the theoretical framework remain valid if the feed-forward network is trained only on states from the environment on which the PSLR will later be tested? Wouldn*t that imply that the prior model distribution is already biased toward the particular environment?
assistant
The paper suggests a practical implementation of Posterior Sampling for Reinforcement Learning (PSLR) using deep networks. The PSLR framework assumes that the model of the environment is sampled from a relatively lower dimensional distribution (for example because of some inherent structure they posses), and exploits this to reduce the sampling complexity to learn an optimal policy, so that the number of samples required is bounded by the complexity of the model distribution, and not by the dimensionality of the state or action space. The paper is well motivated and well written, and the research direction is indeed interesting. I would like to see the implementation section pushing further the limits of the theory by using a more complex posterior model – rather than a simple linear layer – and by testing on more challenging environments, where explicitly exploiting the structure of the non-linear model is essential to solve the task. Regarding the implementation, in Section 4.2 the model is parametrized as s* = W z + s, where W is sampled from the posterior and z is the output of a feed-forward network. Could you further comment on which data the feed-forward network is trained on? Does the theoretical framework remain valid if the feed-forward network is trained only on states from the environment on which the PSLR will later be tested? Wouldn*t that imply that the prior model distribution is already biased toward the particular environment?