Belangrijkste concepten
RiskQ proposes a novel approach for risk-sensitive multi-agent reinforcement learning value factorization, satisfying the RIGM principle for common risk metrics.
Samenvatting
Multi-agent systems face challenges due to environmental uncertainty, varying policies, and partial observability.
Risk-sensitive MARL requires coordinated decentralized policies sensitive to risk.
Existing MARL value factorization methods do not consider risk extensively, impacting performance.
RiskQ introduces a method that models joint return distribution using quantiles of per-agent return distribution utilities.
Extensive experiments show promising results in both risk-sensitive and risk-neutral scenarios.
Directory:
Introduction
Challenges in cooperative multi-agent reinforcement learning (MARL).
Importance of coordinated agent policies in uncertain environments.
Background
Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs).
Value Function Factorization principles like IGM and DIGM.
Distributional RL and different risk measures like VaR and DRM.
Related Work
Overview of existing value factorization methods in MARL.
Progress in risk-sensitive RL for single agents and its adoption in MARL.
Risk-sensitive Value Factorization
Formulation of the RIGM principle for coordination in risk-sensitive MARL.
Introduction of RiskQ to address limitations of existing methods.
Evaluation
Performance evaluation on various environments including MACN, MACF, and SMAC scenarios.
Conclusion
Statistieken
現在のMARL価値因子化手法は、リスクを十分に考慮していない。
RiskQは、各エージェントのリターン分布ユーティリティの分位数を使用して共同リターン分布をモデル化する方法を提案する。