A significant roadblock to the development of principled multi-agent reinforcement learning is the fact that desired solution concepts like Nash equilibria may be intractable to compute. To overcome this obstacle, we take inspiration from behavioral economics and show that -- by imbuing agents with important features of human decision-making like risk aversion and bounded rationality -- a class of risk-averse quantal response equilibria (RQE) become tractable to compute in all $n$-player matrix and finite-horizon Markov games. In particular, we show that they emerge as the endpoint of no-regret learning in suitably adjusted versions of the games. Crucially, the class of computationally tractable RQE is independent of the underlying game structure and only depends on agents' degree of risk-aversion and bounded rationality. To validate the richness of this class of solution concepts we show that it captures peoples' patterns of play in a number of 2-player matrix games previously studied in experimental economics. Furthermore, we give a first analysis of the sample complexity of computing these equilibria in finite-horizon Markov games when one has access to a generative model and validate our findings on a simple multi-agent reinforcement learning benchmark.

通过赋予智能体风险厌恶和有限理性等人类决策要素，我们展示了一类风险厌恶量子响应均衡解（Risk-Averse Quantal Response Equilibria，RQE），不依赖于底层游戏结构而只依赖于智能体的风险厌恶程度和有限理性，在所有n个玩家矩阵和有限时域马尔可夫博弈中可以高效计算。此外，我们还通过实证经济学研究所涉及的许多两人矩阵游戏验证了这类解集的丰富性，并对在有生成模型的有限时域马尔可夫博弈中计算这些均衡的样本复杂度进行了首次分析，同时在简单的多智能体强化学习基准测试中验证了我们的发现。

马尔可夫博弈中应对风险偏好的易处理均衡计算