  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.
