Solly is the first AI to achieve elite human-level play in reduced-format Liar's Poker via self-play actor-critic reinforcement learning, outperforming both world-class humans and large language models on win rate and equity while developing non-exploitable strategies.
URL https://www.science.org/doi/abs/10.1126/sciadv.adg3256
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The paper identifies four missing interfaces (data autolabelling, embodiment retargeting, physics-grounded world models, and video-based reward inference) as the central bottleneck beyond VLA scaling for robot intelligence.
PPO with moderate entropy regularization and current-policy self-play outperforms Monte Carlo Q, SARSA, and Q-learning in a controlled self-play framework for the imperfect-information game Big 2.
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Outbidding and Outbluffing Elite Humans: Mastering Liar's Poker via Self-Play and Reinforcement Learning
Solly is the first AI to achieve elite human-level play in reduced-format Liar's Poker via self-play actor-critic reinforcement learning, outperforming both world-class humans and large language models on win rate and equity while developing non-exploitable strategies.