C2C is a new testbed where LM agents negotiate differently from humans and targeted prompting raises their win rate from 22.2% to 32.7% across 1,100+ games.
arXiv preprint arXiv:2406.02081 , year=
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Odysseus adapts PPO with a turn-level critic and leverages pretrained VLM action priors to train agents achieving at least 3x average game progress over frontier models in long-horizon Super Mario Land.
RL agents in fighting games learn to jointly predict actions and their durations, matching fixed frame-skip performance while favoring repeatable exploitative patterns against scripted bots.
citing papers explorer
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Cooperate to Compete: Strategic Coordination in Multi-Agent Conquest
C2C is a new testbed where LM agents negotiate differently from humans and targeted prompting raises their win rate from 22.2% to 32.7% across 1,100+ games.
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Odysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning
Odysseus adapts PPO with a turn-level critic and leverages pretrained VLM action priors to train agents achieving at least 3x average game progress over frontier models in long-horizon Super Mario Land.
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For How Long Should We Be Punching? Learning Action Duration in Fighting Games
RL agents in fighting games learn to jointly predict actions and their durations, matching fixed frame-skip performance while favoring repeatable exploitative patterns against scripted bots.