Risk-Sensitive Multi-Agent Reinforcement Learning in Network Aggregative Markov Games
<|reference_start|>Risk-Sensitive Multi-Agent Reinforcement Learning in Network Aggregative Markov Games: Classical multi-agent reinforcement learning (MARL) assumes risk neutrality and complete objectivity for agents. However, in settings where agents need to consider or model human economic or social preferences, a notion of risk must be incorporated into the RL optimization problem. This will be of greater importance in MARL where other human or non-human agents are involved, possibly with their own risk-sensitive policies. In this work, we consider risk-sensitive and non-cooperative MARL with cumulative prospect theory (CPT), a non-convex risk measure and a generalization of coherent measures of risk. CPT is capable of explaining loss aversion in humans and their tendency to overestimate/underestimate small/large probabilities. We propose a distributed sampling-based actor-critic (AC) algorithm with CPT risk for network aggregative Markov games (NAMGs), which we call Distributed Nested CPT-AC. Under a set of assumptions, we prove the convergence of the algorithm to a subjective notion of Markov perfect Nash equilibrium in NAMGs. The experimental results show that subjective CPT policies obtained by our algorithm can be different from the risk-neutral ones, and agents with a higher loss aversion are more inclined to socially isolate themselves in an NAMG.<|reference_end|>
@article{ghaemi2024risk-sensitive,
    title={Risk-Sensitive Multi-Agent Reinforcement Learning in Network Aggregative
  Markov Games},
    author={Hafez Ghaemi, Hamed Kebriaei, Alireza Ramezani Moghaddam, Majid Nili
  Ahamdabadi},
    journal={arXiv preprint arXiv:2402.05906},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05906},
    primaryClass={cs.LG cs.AI cs.MA}
}