The Definitive Guide to Policy Gradients in Deep Reinforcement Learning: Theory, Algorithms and Implementations
원문 초록
In recent years, various powerful policy gradient algorithms have been proposed in deep reinforcement learning. While all these algorithms build on the Policy Gradient Theorem, the specific design choices differ significantly across algorithms. We provide a holistic overview of on-policy policy gradient algorithms to facilitate the understanding of both their theoretical foundations and their practical implementations. In this overview, we include a detailed proof of the continuous version of the Policy Gradient Theorem, convergence results and a comprehensive discussion of practical algorithms. We compare the most prominent algorithms on continuous control environments and provide insights on the benefits of regularization. All code is available at https://github.com/Matt00n/PolicyGradientsJax.
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DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
rQdia: Regularizing Q-Value Distributions With Image Augmentation
Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity
Improving Value Estimation Critically Enhances Vanilla Policy Gradient
RLBenchNet: The Right Network for the Right Reinforcement Learning Task
Guided Policy Optimization under Partial Observability
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