NePPO learns a player-independent potential function via a novel objective whose minimization yields an approximate Nash equilibrium for general-sum multi-agent games.
A survey on self-play methods in reinforcement learning
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Tournament-informed task selection in adversarial QD produces higher quality and diversity in coevolved solutions across Pong, cat-and-mouse, and pursuers-evaders games.
A workshop synthesis provides a decomposition framework for RL-cyber environment interfaces and best-practice guidelines for training and evaluating autonomous cyber defence agents.
EvoNash-MARL achieves 19.6% annualized returns on equity allocation from 2014-2024 versus 11.7% for SPY, with evidence of robustness under constraints but no strong statistical superiority per WRC and SPA-lite tests.
citing papers explorer
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NePPO: Near-Potential Policy Optimization for General-Sum Multi-Agent Reinforcement Learning
NePPO learns a player-independent potential function via a novel objective whose minimization yields an approximate Nash equilibrium for general-sum multi-agent games.
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Tournament Informed Adversarial Quality Diversity
Tournament-informed task selection in adversarial QD produces higher quality and diversity in coevolved solutions across Pong, cat-and-mouse, and pursuers-evaders games.
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Building Better Environments for Autonomous Cyber Defence
A workshop synthesis provides a decomposition framework for RL-cyber environment interfaces and best-practice guidelines for training and evaluating autonomous cyber defence agents.
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EvoNash-MARL: A Closed-Loop Multi-Agent Reinforcement Learning Framework for Medium-Horizon Equity Allocation
EvoNash-MARL achieves 19.6% annualized returns on equity allocation from 2014-2024 versus 11.7% for SPY, with evidence of robustness under constraints but no strong statistical superiority per WRC and SPA-lite tests.