Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse

• URL: http://arxiv.org/abs/2206.13714v3
• Date: Fri, 11 Oct 2024 20:43:26 GMT
• Title: Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse
• Authors: James Queeney, Ioannis Ch. Paschalidis, Christos G. Cassandras,
• Abstract summary: We develop a new class of model-free deep reinforcement learning algorithms for data-driven, learning-based control. Our Generalized Policy Improvement algorithms combine the policy improvement guarantees of on-policy methods with the efficiency of sample reuse.
• Score: 15.134707391442236
• License:
• Abstract: We develop a new class of model-free deep reinforcement learning algorithms for data-driven, learning-based control. Our Generalized Policy Improvement algorithms combine the policy improvement guarantees of on-policy methods with the efficiency of sample reuse, addressing a trade-off between two important deployment requirements for real-world control: (i) practical performance guarantees and (ii) data efficiency. We demonstrate the benefits of this new class of algorithms through extensive experimental analysis on a broad range of simulated control tasks.

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