Dream to Control: Learning Behaviors by Latent Imagination

Danijar Hafner Timothy Lillicrap Jimmy Ba Mohammad Norouzi

ICLR 2020 (oral)
NeurIPS Deep RL Workshop 2019 (oral)

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Learned world models summarize an agent’s experience to facilitate learning complex behaviors. While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them. We present Dreamer, a reinforcement learning agent that solves long-horizon tasks from images purely by latent imagination. We efficiently learn behaviors by propagating analytic gradients of learned state values back through trajectories imagined in the compact state space of a learned world model. On 20 challenging visual control tasks, Dreamer exceeds existing approaches in data-efficiency, computation time, and final performance.

Also check out the follow-up project DreamerV2.


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