Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning

Understand

Constrained Markov Decision Process (CMDP) is a natural framework for reinforcement learning tasks with safety constraints, where agents learn a policy that maximizes the long-term reward while satisfying the constraints on the long-term cost.

• A canonical approach for solving CMDPs is the primal-dual method which updates parameters in primal and dual spaces in turn.
• Existing methods for CMDPs only use on-policy data for dual updates, which results in sample inefficiency and slow convergence.
• In this paper, we propose a policy search method for CMDPs called Accelerated Primal-Dual Optimization (APDO), which incorporates an off-policy trained dual variable in the dual update procedure while updating the policy in primal space with on-policy likelihood ratio gradient.