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The document introduces Constrained Policy Optimization (CPO), a novel algorithm for reinforcement learning that incorporates safety constraints during training, ensuring near-constraint satisfaction at each iteration. CPO is designed for high-dimensional control tasks and provides theoretical guarantees based on a new bound relating expected returns of policies to their divergence. The method is demonstrated to effectively train neural network policies while adhering to specified constraints, addressing li

Author

as784

Language

EN