Environment Probing Interaction Policies
A key challenge in reinforcement learning (RL) is environment generalization: a policy trained to solve a task in one environment often fails to solve the same task in a slightly different test environment. A common approach to improve inter-environment transfer is to learn policies that are invariant to the distribution of testing environments. However, we argue that instead of being invariant, the policy should identify the specific nuances of an environment and exploit them to achieve better performance. In this work, we propose the “Environment-Probing” Interaction (EPI) policy, a policy that probes a new environment to extract an implicit understanding of that environment’s behavior. Once this environment-specific information is obtained, it is used as an additional input to a task-specific policy that can now perform environment-conditioned actions to solve a task. To learn these EPI-policies, we present a reward function based on transition predictability. Specifically, a higher reward is given if the trajectory generated by the EPI-policy can be used to better predict transitions. We experimentally show that EPI-conditioned task-specific policies significantly outperform commonly used policy generalization methods on novel testing environments.
 This paper proposes an “Environment-Probing” Interaction (EPI) policy used as an additional input for reinforcement learning (RL). This EPI allows to extract environment representations and implicitly understand the environment in order to improve the generalization on novel testing environments. Pros: This paper is well written and clear and the contribution is relevant to ICLR. Although I am not familiar with RL , the contribution seems novel and the model performances are compared with strong and appropriate baselines.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Environment Probing Interaction Policies
A key challenge in reinforcement learning (RL) is environment generalization: a policy trained to solve a task in one environment often fails to solve the same task in a slightly different test environment. A common approach to improve inter-environment transfer is to learn policies that are invariant to the distribution of testing environments. However, we argue that instead of being invariant, the policy should identify the specific nuances of an environment and exploit them to achieve better performance. In this work, we propose the “Environment-Probing” Interaction (EPI) policy, a policy that probes a new environment to extract an implicit understanding of that environment’s behavior. Once this environment-specific information is obtained, it is used as an additional input to a task-specific policy that can now perform environment-conditioned actions to solve a task. To learn these EPI-policies, we present a reward function based on transition predictability. Specifically, a higher reward is given if the trajectory generated by the EPI-policy can be used to better predict transitions. We experimentally show that EPI-conditioned task-specific policies significantly outperform commonly used policy generalization methods on novel testing environments.
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<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>



 This paper proposes an “Environment-Probing” Interaction (EPI) policy used as an additional input for reinforcement learning (RL). This EPI allows to extract environment representations and implicitly understand the environment in order to improve the generalization on novel testing environments. Pros: This paper is well written and clear and the contribution is relevant to ICLR. Although I am not familiar with RL , the contribution seems novel and the model performances are compared with strong and appropriate baselines.
assistant
 This paper proposes an “Environment-Probing” Interaction (EPI) policy used as an additional input for reinforcement learning (RL). This EPI allows to extract environment representations and implicitly understand the environment in order to improve the generalization on novel testing environments. Pros: This paper is well written and clear and the contribution is relevant to ICLR. Although I am not familiar with RL , the contribution seems novel and the model performances are compared with strong and appropriate baselines.