arXiv (OAI Expanded)
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SafePG: Safe and Globally Optimal Reinforcement Learning with Hard Constraints
We present an optimal and convergent model-free policy gradient (PG) reinforcement learning (RL) framework for controlling nonlinear dynamical systems under hard safety constraints. We first construct a class of stochastic wrapper policies centered around a deterministic controller, thereby enabling
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