On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning

NeurIPSSpotlight2025

Authors
Till Freihaut, Giorgia Ramponi
Venue
NeurIPS 2025
Track
Spotlight

TL;DR

Multi-agent inverse reinforcement learning (MAIRL) aims to recover agent reward functions from expert demonstrations…

Opening excerpt from the authors’ abstract. source

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Topics

reinforcement learning agent

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