Regulatory Focus: Promotion and Prevention Inclinations in Policy Search
The estimation of advantage is crucial for a number of reinforcement learning algorithms, as it directly influences the choices of future paths. In this work, we propose a family of estimates based on the order statistics over the path ensemble, which allows one to flexibly drive the learning process in a promotion focus or prevention focus. On top of this formulation, we systematically study the impacts of different regulatory focuses. Our findings reveal that regulatory focus, when chosen appropriately, can result in significant benefits. In particular, for the environments with sparse rewards, promotion focus would lead to more efficient exploration of the policy space; while for those where individual actions can have critical impacts, prevention focus is preferable. On various benchmarks, including MuJoCo continuous control, Terrain locomotion, Atari games, and sparse-reward environments, the proposed schemes consistently demonstrate improvement over mainstream methods, not only accelerating the learning process but also obtaining substantial performance gains.
This paper focuses on risk-aware reinforcement learning, where an agent could be encouraged to take more risk (high reward, high variance states) or avoid risk (low variance states). Risk control is instantiated by different ways of estimating the advantage of a state (max/min instead of average). Experiments on several environments show good performance of the proposed algorithm. The paper is written clearly and the approach is straightforward. However, it*s unclear what is the objective function the algorithm is optimizing by using a biased estimation of the advantage. It seems to work pretty well in practice, but I wonder how it compares to other risk-sensitive RL algorithms (e.g. those cited in the related work section). Overall, this paper presents a simple heuristic to steer the policy towards risk-seeking / risk-avoiding directions, but could benefit from either more theoretical analysis or more empirical comparison with other methods.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Regulatory Focus: Promotion and Prevention Inclinations in Policy Search
The estimation of advantage is crucial for a number of reinforcement learning algorithms, as it directly influences the choices of future paths. In this work, we propose a family of estimates based on the order statistics over the path ensemble, which allows one to flexibly drive the learning process in a promotion focus or prevention focus. On top of this formulation, we systematically study the impacts of different regulatory focuses. Our findings reveal that regulatory focus, when chosen appropriately, can result in significant benefits. In particular, for the environments with sparse rewards, promotion focus would lead to more efficient exploration of the policy space; while for those where individual actions can have critical impacts, prevention focus is preferable. On various benchmarks, including MuJoCo continuous control, Terrain locomotion, Atari games, and sparse-reward environments, the proposed schemes consistently demonstrate improvement over mainstream methods, not only accelerating the learning process but also obtaining substantial performance gains.
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>



This paper focuses on risk-aware reinforcement learning, where an agent could be encouraged to take more risk (high reward, high variance states) or avoid risk (low variance states). Risk control is instantiated by different ways of estimating the advantage of a state (max/min instead of average). Experiments on several environments show good performance of the proposed algorithm. The paper is written clearly and the approach is straightforward. However, it*s unclear what is the objective function the algorithm is optimizing by using a biased estimation of the advantage. It seems to work pretty well in practice, but I wonder how it compares to other risk-sensitive RL algorithms (e.g. those cited in the related work section). Overall, this paper presents a simple heuristic to steer the policy towards risk-seeking / risk-avoiding directions, but could benefit from either more theoretical analysis or more empirical comparison with other methods.
assistant
This paper focuses on risk-aware reinforcement learning, where an agent could be encouraged to take more risk (high reward, high variance states) or avoid risk (low variance states). Risk control is instantiated by different ways of estimating the advantage of a state (max/min instead of average). Experiments on several environments show good performance of the proposed algorithm. The paper is written clearly and the approach is straightforward. However, it*s unclear what is the objective function the algorithm is optimizing by using a biased estimation of the advantage. It seems to work pretty well in practice, but I wonder how it compares to other risk-sensitive RL algorithms (e.g. those cited in the related work section). Overall, this paper presents a simple heuristic to steer the policy towards risk-seeking / risk-avoiding directions, but could benefit from either more theoretical analysis or more empirical comparison with other methods.