arXiv Machine Learning
JaxMARL-BC: Multi-Agent Reinforcement Learning for Macroeconomic Modelling, in Seconds
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Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates
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Can Reinforcement Learning Efficiently Discover Price Manipulation?
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NashDreamer: Model-Based Reinforcement Learning for Zero-Sum Imperfect-Information Games
NashDreamer is a new model-based reinforcement learning framework designed for two-player zero-sum imperfect-information games. It introduces a centralized Multi-Agent Recurrent State-Space Model that separates environment dynamics from player strategy effects, enabling the use of any policy gradient algorithm while preserving convergence guarantees to Nash equilibria. Experiments on four benchmark games show that NashDreamer achieves significantly better sample efficiency than model-free baselines early in training, and the authors analyze its optimization landscape, noting a potential vulnerability to posterior collapse in stochastic settings.