The content delves into the study of Policy Gradient Methods in the context of Symmetries and State Abstractions. It explores the extension of Markov Decision Process (MDP) homomorphisms to continuous settings, deriving policy gradient theorems for stochastic and deterministic policies. The study focuses on the integration of MDP homomorphisms into policy optimization, showcasing the effectiveness of leveraging approximate symmetries for improved sample efficiency. The paper also introduces a deep actor-critic algorithm based on theoretical results, demonstrating the benefits of state-action abstractions for representation learning in challenging visual control tasks.
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