Interactive Inverse Reinforcement Learning for Cooperative Games
Paper i proceeding, 2022

We study the problem of designing autonomous agents that can learn to cooperate effectively with a potentially suboptimal partner while having no access to the joint reward function. This problem is modeled as a cooperative episodic two-agent Markov decision process. We assume control over only the first of the two agents in a Stackelberg formulation of the game, where the second agent is acting so as to maximise expected utility given the first agent's policy. How should the first agent act in order to learn the joint reward function as quickly as possible and so that the joint policy is as close to optimal as possible? We analyse how knowledge about the reward function can be gained in this interactive two-agent scenario. We show that when the learning agent's policies have a significant effect on the transition function, the reward function can be learned efficiently.


Thomas Kleine Buning

Universitetet i Oslo

Anne-Marie George

Universitetet i Oslo

Christos Dimitrakakis

Chalmers, Data- och informationsteknik, Data Science och AI

Proceedings of Machine Learning Research

26403498 (eISSN)

Vol. 162 2413-2413

38th International Conference on Machine Learning (ICML)
Baltimore, MD, USA,



Annan matematik

Datavetenskap (datalogi)

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