Exploration via Planning for Information about the Optimal Trajectory

• URL: http://arxiv.org/abs/2210.04642v1
• Date: Thu, 6 Oct 2022 20:28:55 GMT
• Title: Exploration via Planning for Information about the Optimal Trajectory
• Authors: Viraj Mehta and Ian Char and Joseph Abbate and Rory Conlin and Mark D. Boyer and Stefano Ermon and Jeff Schneider and Willie Neiswanger
• Abstract summary: We develop a method that allows us to plan for exploration while taking the task and the current knowledge into account. We demonstrate that our method learns strong policies with 2x fewer samples than strong exploration baselines.
• Score: 67.33886176127578
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many potential applications of reinforcement learning (RL) are stymied by the large numbers of samples required to learn an effective policy. This is especially true when applying RL to real-world control tasks, e.g. in the sciences or robotics, where executing a policy in the environment is costly. In popular RL algorithms, agents typically explore either by adding stochasticity to a reward-maximizing policy or by attempting to gather maximal information about environment dynamics without taking the given task into account. In this work, we develop a method that allows us to plan for exploration while taking both the task and the current knowledge about the dynamics into account. The key insight to our approach is to plan an action sequence that maximizes the expected information gain about the optimal trajectory for the task at hand. We demonstrate that our method learns strong policies with 2x fewer samples than strong exploration baselines and 200x fewer samples than model free methods on a diverse set of low-to-medium dimensional control tasks in both the open-loop and closed-loop control settings.

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