Safe adaptation in multiagent competition
This addresses the challenge of maintaining safety and robustness for autonomous robots in competitive multiagent scenarios, representing an incremental improvement over existing adaptation methods.
The paper tackles the problem of safe adaptation in multiagent competition, where agents risk overfitting to specific opponents, and proposes a regularized opponent model to avoid this, resulting in improved robustness and low exploitability in Mujoco experiments.
Achieving the capability of adapting to ever-changing environments is a critical step towards building fully autonomous robots that operate safely in complicated scenarios. In multiagent competitive scenarios, agents may have to adapt to new opponents with previously unseen behaviors by learning from the interaction experiences between the ego-agent and the opponent. However, this adaptation is susceptible to opponent exploitation. As the ego-agent updates its own behavior to exploit the opponent, its own behavior could become more exploitable as a result of overfitting to this specific opponent's behavior. To overcome this difficulty, we developed a safe adaptation approach in which the ego-agent is trained against a regularized opponent model, which effectively avoids overfitting and consequently improves the robustness of the ego-agent's policy. We evaluated our approach in the Mujoco domain with two competing agents. The experiment results suggest that our approach effectively achieves both adaptation to the specific opponent that the ego-agent is interacting with and maintaining low exploitability to other possible opponent exploitation.