Action Noise in Off-Policy Deep Reinforcement Learning: Impact on Exploration and Performance

TL;DR

This paper investigates how different types and scales of action noise affect exploration and performance in off-policy deep reinforcement learning, proposing new measures and heuristics for optimal noise configuration.

Contribution

The study systematically analyzes the impact of Gaussian and Ornstein-Uhlenbeck noise on exploration, introduces a robust state-space coverage measure, and offers heuristic guidelines for noise scheduling.

Findings

Larger noise scales increase state-space coverage but may not improve learning.

Reducing noise scale during training generally enhances performance.

Environment-dependent noise choices are crucial for optimal exploration.

Abstract

Many Deep Reinforcement Learning (D-RL) algorithms rely on simple forms of exploration such as the additive action noise often used in continuous control domains. Typically, the scaling factor of this action noise is chosen as a hyper-parameter and is kept constant during training. In this paper, we focus on action noise in off-policy deep reinforcement learning for continuous control. We analyze how the learned policy is impacted by the noise type, noise scale, and impact scaling factor reduction schedule. We consider the two most prominent types of action noise, Gaussian and Ornstein-Uhlenbeck noise, and perform a vast experimental campaign by systematically varying the noise type and scale parameter, and by measuring variables of interest like the expected return of the policy and the state-space coverage during exploration. For the latter, we propose a novel state-space coverage measure $\operatorname{X}_{\mathcal{U}\text{rel}}$ that is more robust to estimation artifacts caused by points close to the state-space boundary than previously-proposed measures. Larger noise scales generally increase state-space coverage. However, we found that increasing the space coverage using a larger noise scale is often not beneficial. On the contrary, reducing the noise scale over the training process reduces the variance and generally improves the learning performance. We conclude that the best noise type and scale are environment dependent, and based on our observations derive heuristic rules for guiding the choice of the action noise as a starting point for further optimization.