Adversary A3C for Robust Reinforcement Learning

TL;DR

This paper introduces AR-A3C, an adversarial training extension of A3C, which enhances the robustness of reinforcement learning agents against noise and adversarial disturbances through adversarial environment interactions.

Contribution

The paper proposes AR-A3C, a novel adversarial training method that improves the robustness of A3C agents in noisy and adversarial environments.

Findings

AR-A3C outperforms A3C in noisy environments.

AR-A3C maintains stability in real-world experiments.

Adversarial training enhances policy robustness.

Abstract

Asynchronous Advantage Actor Critic (A3C) is an effective Reinforcement Learning (RL) algorithm for a wide range of tasks, such as Atari games and robot control. The agent learns policies and value function through trial-and-error interactions with the environment until converging to an optimal policy. Robustness and stability are critical in RL; however, neural network can be vulnerable to noise from unexpected sources and is not likely to withstand very slight disturbances. We note that agents generated from mild environment using A3C are not able to handle challenging environments. Learning from adversarial examples, we proposed an algorithm called Adversary Robust A3C (AR-A3C) to improve the agent's performance under noisy environments. In this algorithm, an adversarial agent is introduced to the learning process to make it more robust against adversarial disturbances, thereby making it more adaptive to noisy environments. Both simulations and real-world experiments are carried out to illustrate the stability of the proposed algorithm. The AR-A3C algorithm outperforms A3C in both clean and noisy environments.