A Behavior-Aware Approach for Deep Reinforcement Learning in Non-stationary Environments without Known Change Points
This addresses the challenge of maintaining reliable and flexible AI systems in practical scenarios with unpredictable conditions, representing an incremental improvement over existing methods.
The paper tackles the problem of deep reinforcement learning in non-stationary environments by proposing the Behavior-Aware Detection and Adaptation (BADA) framework, which detects changes using Wasserstein distances and adapts policies through behavior regularization, resulting in better performance compared to current algorithms.
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.