合作博弈的交互式逆强化学习
Interactive Inverse Reinforcement Learning for Cooperative Games
摘要：
我们研究的问题，设计自主智能体，可以学习有效地与潜在的次优合作伙伴，而没有访问的联合奖励功能。这个问题被建模为一个合作的情节两代理马尔可夫决策过程。我们假设控制只有第一个的两个代理在Stackelberg制定的游戏，其中第二个代理的行为，以最大限度地提高预期效用的第一个代理的政策。第一个智能体应该如何行动，以尽快学习联合奖励函数，使联合策略尽可能接近最优？我们分析如何在这种交互式的两个代理的情况下，可以获得有关奖励函数的知识。我们表明，当学习代理的政策有显着的影响的过渡函数，奖励功能可以有效地学习。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
