Opponent Modeling

Opponent modeling focuses on predicting and adapting to the behavior of other agents in multi-agent systems, aiming to improve an agent's performance in competitive or cooperative scenarios. Current research emphasizes developing efficient algorithms, such as those based on hierarchical modeling, Monte Carlo Tree Search, and various deep reinforcement learning architectures (e.g., Double Deep Q-Networks, Actor-Critic methods), to accurately predict opponent strategies in real-time and adapt accordingly. This field is crucial for advancing artificial intelligence in domains like autonomous driving, robotics, and strategic game playing, where understanding and responding to other agents' actions is essential for success and safety.