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Fall 2024
Planning and goal-conditioned reinforcement learning aim to create more efficient and scalable methods for complex, long-horizon tasks. These approaches break tasks into manageable subgoals and leverage prior knowledge to guide learning. However, learned models may predict inaccurate next states...
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Fall 2022
This thesis investigates a new approach to model-based reinforcement learning using background planning: mixing (approximate) dynamic programming updates and model-free updates, similar to the Dyna architecture. Background planning with learned models is often worse than model-free alternatives,...