Summary of Risk-sensitive Multi-agent Reinforcement Learning in Network Aggregative Markov Games, by Hafez Ghaemi et al.
Risk-Sensitive Multi-Agent Reinforcement Learning in Network Aggregative Markov Games
by Hafez Ghaemi, Hamed Kebriaei, Alireza Ramezani Moghaddam, Majid Nili Ahamdabadi
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel approach to multi-agent reinforcement learning (MARL) that incorporates risk-sensitivity and non-cooperative behavior. The traditional MARL framework assumes risk neutrality and objectivity for agents, but this can be limiting when agents need to consider human or social preferences. To address this, the authors develop a distributed sampling-based actor-critic algorithm using cumulative prospect theory (CPT), a risk measure that can explain loss aversion in humans. They apply their algorithm to network aggregative Markov games (NAMGs) and show that it converges to a subjective notion of Markov perfect Nash equilibrium. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is important because it allows agents in MARL settings to consider the risk of taking different actions, which can be crucial when other agents have their own risk-sensitive policies. The authors’ algorithm uses cumulative prospect theory, which can help explain why humans tend to overestimate or underestimate small or large probabilities. This can lead to more realistic and diverse outcomes in MARL scenarios. |
Keywords
* Artificial intelligence * Reinforcement learning
