Stochastic Neural Networks for Hierarchical Reinforcement Learning
Deep reinforcement learning has achieved many impressive results in recent years. However, tasks with sparse rewards or long horizons continue to pose significant challenges. To tackle these important problems, we propose a general framework that first learns useful skills in a pre-training environment, and then leverages the acquired skills for learning faster in downstream tasks. Our approach brings together some of the strengths of intrinsic motivation and hierarchical methods: the learning of useful skill is guided by a single proxy reward, the design of which requires very minimal domain knowledge about the downstream tasks. Then a high-level policy is trained on top of these skills, providing a significant improvement of the exploration and allowing to tackle sparse rewards in the downstream tasks. To efficiently pre-train a large span of skills, we use Stochastic Neural Networks combined with an information-theoretic regularizer. Our experiments show that this combination is effective in learning a wide span of interpretable skills in a sample-efficient way, and can significantly boost the learning performance uniformly across a wide range of downstream tasks.
Interesting work on hierarchical control, similar to the work of Heess et al. Experiments are strong and manage to complete benchmarks that previous work could not. Analysis of the experiments is a bit on the weaker side. (1) Like other reviewers, I find the use of the term ‘intrinsic’ motivation somewhat inappropriate (mostly because of its current meaning in RL). Pre-training robots with locomotion by rewarding speed (or rewarding grasping for a manipulating arm) is very geared towards the tasks they will later accomplish. The pre-training tasks from Heess et al., while not identical, are similar. (2) The Mutual Information regularization is elegant and works generally well, but does not seem to help in the more complex mazes 1,2 and 3. The authors note this - is there any interpretation or analysis for this result? (3) The factorization between S_agent and S_rest should be clearly detailed in the paper. Duan et al specify S_agent, but for replicability, S_rest should be clearly specified as well - did I miss it? (4) It would be interesting to provide some analysis of the switching behavior of the agent. More generally, some further analysis of the policies (failure modes, effects of switching time on performance) would have been welcome.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Stochastic Neural Networks for Hierarchical Reinforcement Learning
Deep reinforcement learning has achieved many impressive results in recent years. However, tasks with sparse rewards or long horizons continue to pose significant challenges. To tackle these important problems, we propose a general framework that first learns useful skills in a pre-training environment, and then leverages the acquired skills for learning faster in downstream tasks. Our approach brings together some of the strengths of intrinsic motivation and hierarchical methods: the learning of useful skill is guided by a single proxy reward, the design of which requires very minimal domain knowledge about the downstream tasks. Then a high-level policy is trained on top of these skills, providing a significant improvement of the exploration and allowing to tackle sparse rewards in the downstream tasks. To efficiently pre-train a large span of skills, we use Stochastic Neural Networks combined with an information-theoretic regularizer. Our experiments show that this combination is effective in learning a wide span of interpretable skills in a sample-efficient way, and can significantly boost the learning performance uniformly across a wide range of downstream tasks.
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>



Interesting work on hierarchical control, similar to the work of Heess et al. Experiments are strong and manage to complete benchmarks that previous work could not. Analysis of the experiments is a bit on the weaker side. (1) Like other reviewers, I find the use of the term ‘intrinsic’ motivation somewhat inappropriate (mostly because of its current meaning in RL). Pre-training robots with locomotion by rewarding speed (or rewarding grasping for a manipulating arm) is very geared towards the tasks they will later accomplish. The pre-training tasks from Heess et al., while not identical, are similar. (2) The Mutual Information regularization is elegant and works generally well, but does not seem to help in the more complex mazes 1,2 and 3. The authors note this - is there any interpretation or analysis for this result? (3) The factorization between S_agent and S_rest should be clearly detailed in the paper. Duan et al specify S_agent, but for replicability, S_rest should be clearly specified as well - did I miss it? (4) It would be interesting to provide some analysis of the switching behavior of the agent. More generally, some further analysis of the policies (failure modes, effects of switching time on performance) would have been welcome.
assistant
Interesting work on hierarchical control, similar to the work of Heess et al. Experiments are strong and manage to complete benchmarks that previous work could not. Analysis of the experiments is a bit on the weaker side. (1) Like other reviewers, I find the use of the term ‘intrinsic’ motivation somewhat inappropriate (mostly because of its current meaning in RL). Pre-training robots with locomotion by rewarding speed (or rewarding grasping for a manipulating arm) is very geared towards the tasks they will later accomplish. The pre-training tasks from Heess et al., while not identical, are similar. (2) The Mutual Information regularization is elegant and works generally well, but does not seem to help in the more complex mazes 1,2 and 3. The authors note this - is there any interpretation or analysis for this result? (3) The factorization between S_agent and S_rest should be clearly detailed in the paper. Duan et al specify S_agent, but for replicability, S_rest should be clearly specified as well - did I miss it? (4) It would be interesting to provide some analysis of the switching behavior of the agent. More generally, some further analysis of the policies (failure modes, effects of switching time on performance) would have been welcome.