Altruism and Fair Objective in Mixed-Motive Markov games
Cooperation is fundamental for society's viability, as it enables the emergence of structure within heterogeneous groups that seek collective well-being. However, individuals are inclined to defect in order to benefit from the group's cooperation without contributing the associated costs, thus leading to unfair situations. In game theory, social dilemmas entail this dichotomy between individual interest and collective outcome. The most dominant approach to multi-agent cooperation is the utilitarian welfare which can produce efficient highly inequitable outcomes. This paper proposes a novel framework to foster fairer cooperation by replacing the standard utilitarian objective with Proportional Fairness. We introduce a fair altruistic utility for each agent, defined on the individual log-payoff space and derive the analytical conditions required to ensure cooperation in classic social dilemmas. We then extend this framework to sequential settings by defining a Fair Markov Game and deriving novel fair Actor-Critic algorithms to learn fair policies. Finally, we evaluate our method in various social dilemma environments.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games
Real-world multi-agent scenarios often involve mixed motives, demanding altruistic agents capable of self-protection against potential exploitation. However, existing approaches often struggle to achieve both objectives.…
counterfactualCounterfactual ReasoningFairnessMulti-agent Reinforcement LearningAdaptive Punishment for Cooperation in Mixed-Motive Games
Mixed-motive scenarios are ubiquitous in real-world multi-agent interactions, where self-interested agents often defect for immediate rewards, overlooking the potential of altruistic cooperation to improve long-term gain…
Social diversity and social preferences in mixed-motive reinforcement learning
Recent research on reinforcement learning in pure-conflict and pure-common interest games has emphasized the importance of population heterogeneity. In contrast, studies of reinforcement learning in mixed-motive games ha…
Diversityreinforcement-learningReinforcement LearningReinforcement Learning (RL)Learning to Negotiate via Voluntary Commitment
The partial alignment and conflict of autonomous agents lead to mixed-motive scenarios in many real-world applications. However, agents may fail to cooperate in practice even when cooperation yields a better outcome. One…
Aligning Individual and Collective Objectives in Multi-Agent Cooperation
Among the research topics in multi-agent learning, mixed-motive cooperation is one of the most prominent challenges, primarily due to the mismatch between individual and collective goals. The cutting-edge research is foc…
SMACSMAC+StarcraftStarcraft II