Representation-Driven Reinforcement Learning

• URL: http://arxiv.org/abs/2305.19922v2
• Date: Sat, 17 Jun 2023 19:29:21 GMT
• Title: Representation-Driven Reinforcement Learning
• Authors: Ofir Nabati, Guy Tennenholtz and Shie Mannor
• Abstract summary: We present a representation-driven framework for reinforcement learning. By representing policies as estimates of their expected values, we leverage techniques from contextual bandits to guide exploration and exploitation. We demonstrate the effectiveness of this framework through its application to evolutionary and policy gradient-based approaches.
• Score: 57.44609759155611
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a representation-driven framework for reinforcement learning. By representing policies as estimates of their expected values, we leverage techniques from contextual bandits to guide exploration and exploitation. Particularly, embedding a policy network into a linear feature space allows us to reframe the exploration-exploitation problem as a representation-exploitation problem, where good policy representations enable optimal exploration. We demonstrate the effectiveness of this framework through its application to evolutionary and policy gradient-based approaches, leading to significantly improved performance compared to traditional methods. Our framework provides a new perspective on reinforcement learning, highlighting the importance of policy representation in determining optimal exploration-exploitation strategies.

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