4 papers
Towards Learning Representations of Policies in Two-Player Zero-Sum Imperfect-Information Games
Kevin Wang, Kevin Yang, Arjun Prakash +1
We investigate the problem of learning useful policy representations (embeddings) in two-player zero-sum imperfect-information games. We make three contributions: First, we introdu…
MEMO: Memory-Augmented Model Context Optimization for Robust Multi-Turn Multi-Agent LLM Games
Yunfei Xie, Kevin Wang, Bobby Cheng +9
Multi-turn, multi-agent LLM game evaluations often exhibit substantial run-to-run variance. In long-horizon interactions, small early deviations compound across turns and are ampli…
1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities
Kevin Wang, Ishaan Javali, MichaÅ Bortkiewicz +2
Scaling up self-supervised learning has driven breakthroughs in language and vision, yet comparable progress has remained elusive in reinforcement learning (RL). In this paper, we…
Approximating Nash Equilibria in General-Sum Games via Meta-Learning
David Sychrovský, Christopher Solinas, Revan MacQueen +4
Nash equilibrium is perhaps the best-known solution concept in game theory. Such a solution assigns a strategy to each player which offers no incentive to unilaterally deviate. Whi…