A Theoretical Justification for Asymmetric Actor-Critic Algorithms

• URL: http://arxiv.org/abs/2501.19116v1
• Date: Fri, 31 Jan 2025 13:20:05 GMT
• Title: A Theoretical Justification for Asymmetric Actor-Critic Algorithms
• Authors: Gaspard Lambrechts, Damien Ernst, Aditya Mahajan,
• Abstract summary: We propose a justification for asymmetric actor-critic algorithms with linear function approximators. The resulting finite-time bound reveals that the asymmetric critic eliminates an error term arising from aliasing in the agent state.
• Score: 2.9071167862893605
• License:
• Abstract: In reinforcement learning for partially observable environments, many successful algorithms were developed within the asymmetric learning paradigm. This paradigm leverages additional state information available at training time for faster learning. Although the proposed learning objectives are usually theoretically sound, these methods still lack a theoretical justification for their potential benefits. We propose such a justification for asymmetric actor-critic algorithms with linear function approximators by adapting a finite-time convergence analysis to this setting. The resulting finite-time bound reveals that the asymmetric critic eliminates an error term arising from aliasing in the agent state.

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