Actor-critic reinforcement learning methods are reformulated as mirror descent on the occupancy manifold, and a Hessian-based update is proposed for nonlinear and constrained objectives.
Mirror descent policy optimization
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The Geometry of Nonlinear Reinforcement Learning
Actor-critic reinforcement learning methods are reformulated as mirror descent on the occupancy manifold, and a Hessian-based update is proposed for nonlinear and constrained objectives.