Relative Entropy Regularized Policy Iteration

TL;DR

This paper introduces a novel off-policy actor-critic reinforcement learning algorithm that combines stochastic search and value function learning, demonstrating superior performance on multiple continuous control benchmarks.

Contribution

It proposes a flexible, three-step policy iteration method that integrates ideas from black-box optimization and RL inference, extending existing algorithms like MPO and CMA-ES.

Findings

Achieved state-of-the-art results on 31 continuous control tasks.

Demonstrated effectiveness with limited compute and a single hyperparameter set.

Validated the method's versatility across diverse RL environments.

Abstract

We present an off-policy actor-critic algorithm for Reinforcement Learning (RL) that combines ideas from gradient-free optimization via stochastic search with learned action-value function. The result is a simple procedure consisting of three steps: i) policy evaluation by estimating a parametric action-value function; ii) policy improvement via the estimation of a local non-parametric policy; and iii) generalization by fitting a parametric policy. Each step can be implemented in different ways, giving rise to several algorithm variants. Our algorithm draws on connections to existing literature on black-box optimization and 'RL as an inference' and it can be seen either as an extension of the Maximum a Posteriori Policy Optimisation algorithm (MPO) [Abdolmaleki et al., 2018a], or as an extension of Trust Region Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) [Abdolmaleki et al., 2017b; Hansen et al., 1997] to a policy iteration scheme. Our comparison on 31 continuous control tasks from parkour suite [Heess et al., 2017], DeepMind control suite [Tassa et al., 2018] and OpenAI Gym [Brockman et al., 2016] with diverse properties, limited amount of compute and a single set of hyperparameters, demonstrate the effectiveness of our method and the state of art results. Videos, summarizing results, can be found at goo.gl/HtvJKR .