Multi-Agent Inverse Reinforcement Learning: Suboptimal Demonstrations and Alternative Solution Concepts
This research was recently completed within the AI Safety division of the Stanford Existential Risk Initiative and concerns methods for reward learning in multi-agent systems. Abstract: Multi-agent inverse reinforcement learning (MIRL) can be used to learn reward functions from agents in social environments. To model realistic social dynamics, MIRL methods...
Sep 7, 20215