SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning
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Model-based reinforcement learning (RL) methods can be broadly categorized as global model methods, which depend on learning models that provide sensible predictions in a wide range of states, or local model methods, which iteratively refit simple models that are used for policy improvement. While predicting future states that will result from the current actions is difficult, local model methods only attempt to understand system dynamics in the neighborhood of the current policy, making it possible to produce local improvements without ever learning to predict accurately far into the future. The main idea in this paper is that we can learn representations that make it easy to retrospectively infer simple dynamics given the data from the current policy, thus enabling local models to be used for policy learning in complex systems. To that end, we focus on learning representations with probabilistic graphical model (PGM) structure, which allows us to devise an efficient local model method that infers dynamics from real-world rollouts with the PGM as a global prior. We compare our method to other model-based and model-free RL methods on a suite of robotics tasks, including manipulation tasks on a real Sawyer robotic arm directly from camera images. Videos of our results are available at https://sites.google.com/view/solar-iclips
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