GRAC: Self-Guided and Self-Regularized Actor-Critic

TL;DR

GRAC introduces a self-regularized TD-learning and a self-guided policy improvement method, enabling more robust and efficient deep reinforcement learning without the need for target networks, and achieves state-of-the-art results.

Contribution

It proposes a novel actor-critic algorithm that eliminates the need for target networks through self-regularization and enhances policy improvement with zero-order optimization.

Findings

Achieves or outperforms state-of-the-art on OpenAI gym tasks

Demonstrates robustness to local noise in Q-function approximation

Eliminates the need for target networks in deep RL

Abstract

Deep reinforcement learning (DRL) algorithms have successfully been demonstrated on a range of challenging decision making and control tasks. One dominant component of recent deep reinforcement learning algorithms is the target network which mitigates the divergence when learning the Q function. However, target networks can slow down the learning process due to delayed function updates. Our main contribution in this work is a self-regularized TD-learning method to address divergence without requiring a target network. Additionally, we propose a self-guided policy improvement method by combining policy-gradient with zero-order optimization to search for actions associated with higher Q-values in a broad neighborhood. This makes learning more robust to local noise in the Q function approximation and guides the updates of our actor network. Taken together, these components define GRAC, a novel self-guided and self-regularized actor critic algorithm. We evaluate GRAC on the suite of OpenAI gym tasks, achieving or outperforming state of the art in every environment tested.