Learning Latent Dynamics for Planning from Pixels
Read OriginalThis research paper presents the Deep Planning Network (PlaNet), a model-based agent that learns a latent dynamics model directly from pixel observations. It uses a combination of deterministic and stochastic transitions with a novel multi-step variational inference objective called latent overshooting. PlaNet solves complex continuous control tasks with sparse rewards and partial observability, achieving data efficiency far superior to model-free methods while matching or exceeding their final performance.
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