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Passive observational data, such as human videos, is abundant and rich ininformation, yet remains largely untapped by current RL methods. Perhapssurprisingly, we show that passive data, despite not having reward or actionlabels, can still be used to learn features that accelerate downstream RL. Ourapproach learns from passive data by modeling intentions: measuring how thelikelihood of future outcomes change when the agent acts to achieve aparticular task. We propose a temporal difference learning objective to learnabout intentions, resulting in an algorithm similar to conventional RL, butwhich learns entirely from passive data. When optimizing this objective, ouragent simultaneously learns representations of states, of policies, and ofpossible outcomes in an environment, all from raw observational data. Boththeoretically and empirically, this scheme learns features amenable for valueprediction for downstream tasks, and our experiments demonstrate the ability tolearn from many forms of passive data, including cross-embodiment video dataand YouTube videos.

Reinforcement Learning from Passive Data via Latent Intentions