A Behavior-Aware Approach for Deep Reinforcement Learning in Non-stationary Environments without Known Change Points

TL;DR

This paper presents BADA, a novel framework that detects environmental changes and adapts policies in non-stationary settings by analyzing behavioral variations with Wasserstein distances, improving reinforcement learning robustness.

Contribution

Introducing Behavior-Aware Detection and Adaptation (BADA), a new method that detects environment changes through behavior analysis and adapts policies without manual thresholds.

Findings

BADA outperforms existing algorithms in non-stationary environments.

Behavior analysis effectively detects environmental changes.

Adaptive policies maintain higher performance over time.

Abstract

Deep reinforcement learning is used in various domains, but usually under the assumption that the environment has stationary conditions like transitions and state distributions. When this assumption is not met, performance suffers. For this reason, tracking continuous environmental changes and adapting to unpredictable conditions is challenging yet crucial because it ensures that systems remain reliable and flexible in practical scenarios. Our research introduces Behavior-Aware Detection and Adaptation (BADA), an innovative framework that merges environmental change detection with behavior adaptation. The key inspiration behind our method is that policies exhibit different global behaviors in changing environments. Specifically, environmental changes are identified by analyzing variations between behaviors using Wasserstein distances without manually set thresholds. The model adapts to the new environment through behavior regularization based on the extent of changes. The results of a series of experiments demonstrate better performance relative to several current algorithms. This research also indicates significant potential for tackling this long-standing challenge.