Coordinate Ascent for Off-Policy RL with Global Convergence Guarantees
This addresses a key bottleneck in off-policy RL for researchers and practitioners by offering a method that avoids distribution correction, though it appears incremental as it builds on coordinate ascent ideas.
The paper tackles the distribution mismatch issue in off-policy reinforcement learning by introducing Coordinate Ascent Policy Optimization (CAPO), which decouples policy improvement from behavior policy state distribution without using policy gradients, and demonstrates competitive performance in experiments.
We revisit the domain of off-policy policy optimization in RL from the perspective of coordinate ascent. One commonly-used approach is to leverage the off-policy policy gradient to optimize a surrogate objective -- the total discounted in expectation return of the target policy with respect to the state distribution of the behavior policy. However, this approach has been shown to suffer from the distribution mismatch issue, and therefore significant efforts are needed for correcting this mismatch either via state distribution correction or a counterfactual method. In this paper, we rethink off-policy learning via Coordinate Ascent Policy Optimization (CAPO), an off-policy actor-critic algorithm that decouples policy improvement from the state distribution of the behavior policy without using the policy gradient. This design obviates the need for distribution correction or importance sampling in the policy improvement step of off-policy policy gradient. We establish the global convergence of CAPO with general coordinate selection and then further quantify the convergence rates of several instances of CAPO with popular coordinate selection rules, including the cyclic and the randomized variants of CAPO. We then extend CAPO to neural policies for a more practical implementation. Through experiments, we demonstrate that CAPO provides a competitive approach to RL in practice.