Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning
Exploration in complex domains is a key challenge in reinforcement learning, especially for tasks with very sparse rewards. Recent successes in deep reinforcement learning have been achieved mostly using simple heuristic exploration strategies such as -greedy action selection or Gaussian control noise, but there are many tasks where these methods are insufficient to make any learning progress. Here, we consider more complex heuristics: efficient and scalable exploration strategies that maximize a notion of an agent*s surprise about its experiences via intrinsic motivation. We propose to learn a model of the MDP transition probabilities concurrently with the policy, and to form intrinsic rewards that approximate the KL-divergence of the true transition probabilities from the learned model. One of our approximations results in using surprisal as intrinsic motivation, while the other gives the -step learning progress. We show that our incentives enable agents to succeed in a wide range of environments with high-dimensional state spaces and very sparse rewards, including continuous control tasks and games in the Atari RAM domain, outperforming several other heuristic exploration techniques.
The authors present a novel approach to surprise-based intrinsic motivation in deep reinforcement learning. The authors clearly explain the difference from other recent approaches to intrinsic motivation and back up their method with results from a broad class of discrete and continuous action domains. They present two tractable approximations to their framework - one which ignores the stochasticity of the true environmental dynamics, and one which approximates the rate of information gain (somewhat similar to Schmidhuber*s formal theory of creativity, fun and intrinsic motivation). The results of this exploration bonus when added to TRPO are generally better than standard TRPO. However, I would have appreciated a more thorough comparison against other recent work on intrinsic motivation. For instance, Bellemare et al 2016 recently achieved significant performance gains on challenging Atari games like Montezuma*s Revenge by combining DQN with an exploration bonus, however Montezuma*s Revenge is not presented as an experiment here. Such comparisons would significantly improve the strength of the paper.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning
Exploration in complex domains is a key challenge in reinforcement learning, especially for tasks with very sparse rewards. Recent successes in deep reinforcement learning have been achieved mostly using simple heuristic exploration strategies such as -greedy action selection or Gaussian control noise, but there are many tasks where these methods are insufficient to make any learning progress. Here, we consider more complex heuristics: efficient and scalable exploration strategies that maximize a notion of an agent*s surprise about its experiences via intrinsic motivation. We propose to learn a model of the MDP transition probabilities concurrently with the policy, and to form intrinsic rewards that approximate the KL-divergence of the true transition probabilities from the learned model. One of our approximations results in using surprisal as intrinsic motivation, while the other gives the -step learning progress. We show that our incentives enable agents to succeed in a wide range of environments with high-dimensional state spaces and very sparse rewards, including continuous control tasks and games in the Atari RAM domain, outperforming several other heuristic exploration techniques.
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 authors present a novel approach to surprise-based intrinsic motivation in deep reinforcement learning. The authors clearly explain the difference from other recent approaches to intrinsic motivation and back up their method with results from a broad class of discrete and continuous action domains. They present two tractable approximations to their framework - one which ignores the stochasticity of the true environmental dynamics, and one which approximates the rate of information gain (somewhat similar to Schmidhuber*s formal theory of creativity, fun and intrinsic motivation). The results of this exploration bonus when added to TRPO are generally better than standard TRPO. However, I would have appreciated a more thorough comparison against other recent work on intrinsic motivation. For instance, Bellemare et al 2016 recently achieved significant performance gains on challenging Atari games like Montezuma*s Revenge by combining DQN with an exploration bonus, however Montezuma*s Revenge is not presented as an experiment here. Such comparisons would significantly improve the strength of the paper.
assistant
The authors present a novel approach to surprise-based intrinsic motivation in deep reinforcement learning. The authors clearly explain the difference from other recent approaches to intrinsic motivation and back up their method with results from a broad class of discrete and continuous action domains. They present two tractable approximations to their framework - one which ignores the stochasticity of the true environmental dynamics, and one which approximates the rate of information gain (somewhat similar to Schmidhuber*s formal theory of creativity, fun and intrinsic motivation). The results of this exploration bonus when added to TRPO are generally better than standard TRPO. However, I would have appreciated a more thorough comparison against other recent work on intrinsic motivation. For instance, Bellemare et al 2016 recently achieved significant performance gains on challenging Atari games like Montezuma*s Revenge by combining DQN with an exploration bonus, however Montezuma*s Revenge is not presented as an experiment here. Such comparisons would significantly improve the strength of the paper.