Bias Correction in Reinforcement Learning via the Deterministic Policy Gradient Method for MPC-Based Policies

摘要

In this paper, we discuss the implementation of the Deterministic Policy Gradient using the Actor-Critic technique based on linear compatible advantage function approximations in the context of constrained policies. We focus on MPC-based policies, though the discussion is general. We show that in that context, the classic linear compatible advantage function approximation fails to deliver a correct policy gradient due to the exploration becoming distorted by the constraints, and we propose a generalized linear compatible advantage function approximation that corrects the problem. We show that this correction requires an estimation of the mean and covariance of the constrained exploration. The validity of that generalization is formally established and demonstrated on a simple example.