Uncertainty - sensitive learning and planning with ensembles
We propose a reinforcement learning framework for discrete environments in which an agent optimizes its behavior on two timescales. For the short one, it uses tree search methods to perform tactical decisions. The long strategic level is handled with an ensemble of value functions learned using -like backups. Combining these two techniques brings synergies. The planning module performs 	extit{what-if} analysis allowing to avoid short-term pitfalls and boost backups of the value function. Notably, our method performs well in environments with sparse rewards where standard backups fail. On the other hand, the value functions compensate for inherent short-sightedness of planning. Importantly, we use ensembles to measure the epistemic uncertainty of value functions. This serves two purposes: a) it stabilizes planning, b) it guides exploration. We evaluate our methods on discrete environments with sparse rewards: the Deep sea chain environment, toy Montezuma*s Revenge, and Sokoban. In all the cases, we obtain speed-up of learning and boost to the final performance.
The paper proposes an approach blending model-based, model-free methods and utilizing risk-sensitivity information in ensembles as part of the value estimation and exploration process. The exploration is based on risk- sensitivity measures such as moments and relative majority vote. There is a lot of work currently trying to marry the model free with model based approaches for integrated planning and learning as the authors have mentioned in the related work section of the paper and also called out similar methods and techniques. The authors have provided evidence via experiments in three environments and shown good results of using this blended approach. Code is also provided for others to further carry out explorations in this research area.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Uncertainty - sensitive learning and planning with ensembles
We propose a reinforcement learning framework for discrete environments in which an agent optimizes its behavior on two timescales. For the short one, it uses tree search methods to perform tactical decisions. The long strategic level is handled with an ensemble of value functions learned using -like backups. Combining these two techniques brings synergies. The planning module performs 	extit{what-if} analysis allowing to avoid short-term pitfalls and boost backups of the value function. Notably, our method performs well in environments with sparse rewards where standard backups fail. On the other hand, the value functions compensate for inherent short-sightedness of planning. Importantly, we use ensembles to measure the epistemic uncertainty of value functions. This serves two purposes: a) it stabilizes planning, b) it guides exploration. We evaluate our methods on discrete environments with sparse rewards: the Deep sea chain environment, toy Montezuma*s Revenge, and Sokoban. In all the cases, we obtain speed-up of learning and boost to the final performance.
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 proposes an approach blending model-based, model-free methods and utilizing risk-sensitivity information in ensembles as part of the value estimation and exploration process. The exploration is based on risk- sensitivity measures such as moments and relative majority vote. There is a lot of work currently trying to marry the model free with model based approaches for integrated planning and learning as the authors have mentioned in the related work section of the paper and also called out similar methods and techniques. The authors have provided evidence via experiments in three environments and shown good results of using this blended approach. Code is also provided for others to further carry out explorations in this research area.
assistant
The paper proposes an approach blending model-based, model-free methods and utilizing risk-sensitivity information in ensembles as part of the value estimation and exploration process. The exploration is based on risk- sensitivity measures such as moments and relative majority vote. There is a lot of work currently trying to marry the model free with model based approaches for integrated planning and learning as the authors have mentioned in the related work section of the paper and also called out similar methods and techniques. The authors have provided evidence via experiments in three environments and shown good results of using this blended approach. Code is also provided for others to further carry out explorations in this research area.