arXiv:2606.16759v1 Announce Type: new Abstract: We study inverse reinforcement learning for discrete-time, infinite-horizon mean-field games (MFGs) under an average-reward criterion. Expert demonstrations are assumed to arise from a stationary mean-field equilibrium under an unknown reward, and the goal is to recover a policy explaining the observed behaviour via the maximum causal entropy principle. We formulate the inverse problem by enforcing consistency with the expert mean-field term and long-run feature expectations, treating two reward classes within a unified occupation-measure framewo
The continuous advancements in AI research, particularly in reinforcement learning and multi-agent systems, drive the exploration of more complex decision-making frameworks like mean-field games.
This research is crucial for developing AI systems capable of operating autonomously and optimally in environments with many interacting agents, which is foundational for advanced AI agents and robotics.
This theoretical work provides a method for inferring reward functions in complex multi-agent systems, improving the ability to design and understand intelligent behavior in large-scale AI applications.
- · AI researchers
- · Robotics developers
- · Developers of multi-agent systems
- · N/A
Improved inverse reinforcement learning techniques for complex AI systems.
More robust and adaptable AI agents capable of operating in dynamic, multi-entity environments.
Accelerated development of autonomous AI systems for various applications, from logistics to intelligent infrastructure.
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