Adapting to Changes in Agent Behavior via Finite-Depth Policy Sensitivity
The method estimates how a reinforcement learning policy changes when another agent's behavior parameters shift, avoiding massive new interaction data. It achieves this by approximating complex derivatives using information from a reference environment and truncating the derivative propagation depth. This provides an efficient way to adapt policies to behavioral changes without extensive retraining.