Agents
Opponent Modeling with In-context Search
Opponent modeling is a longstanding research topic aimed at enhancing decision-making by modeling information about opponents in multi-agent environments. However, existing approaches often face challenges such as having difficulty generalizing to unknown opponent policies and conducting unstable performance.
Rethinking Exploration in Reinforcement Learning with Effective Metric-Based Exploration Bonus
Additionally, methods that utilize the bisimulation metric for evaluating state discrepancies face a theory-practice gap due to improper approximations in metric learning, particularly struggling with hard exploration tasks.
No-Regret Learning for Fair Multi-Agent Social Welfare Optimization
We consider the problem of online multi-agent Nash social welfare (NSW) maximization.
