agent interactions
Agent interactions explore dynamics among multiple learning agents or stakeholders in a system, such as in multi-agent reinforcement learning. Understanding these interactions helps design cooperative or competitive strategies and predict system behavior.
- Flow Matching-Based Autonomous Driving Planning with Advanced Interactive Behavior Modeling
- High-order Interactions Modeling for Interpretable Multi-Agent Q-Learning
- Last Iterate Convergence in Monotone Mean Field Games
- Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks
- Mean-Field Sampling for Cooperative Multi-Agent Reinforcement Learning
- Multi-Agent Debate for LLM Judges with Adaptive Stability Detection