Core Concepts
RiskQ proposes a novel approach for risk-sensitive multi-agent reinforcement learning value factorization, satisfying the RIGM principle for common risk metrics.
Abstract
The content introduces RiskQ, a method for risk-sensitive multi-agent reinforcement learning. It discusses the challenges in coordinating agents in risk-sensitive environments and proposes the Risk-sensitive Individual-Global-Maximization (RIGM) principle. RiskQ models joint return distribution by combining per-agent return distribution utilities and satisfies the RIGM principle for various risk metrics. Extensive experiments demonstrate promising results across different scenarios.
Directory:
- Abstract
- Introduces Multi-Agent Reinforcement Learning (MARL) challenges.
- Proposes the RIGM principle and introduces RiskQ.
- Introduction
- Discusses challenges in cooperative MARL.
- Background
- Explains Dec-POMDPs and Value Function Factorization.
- Related Work
- Reviews existing value factorization methods.
- Risk-sensitive Value Factorization
- Introduces the RIGM principle and explains how RiskQ addresses it.
- Evaluation
- Evaluates RiskQ performance in various scenarios.
- Ablation Study and Discussion
- Analyzes different designs of RiskQ and their impact on performance.
- Conclusion
- Summarizes the importance of coordinated risk-sensitive cooperation.
Stats
"Risk refers to the uncertainty of future outcomes in multi-agent systems."
"RiskQ can obtain promising performance through extensive experiments."