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Fall 2022
In this thesis, we investigate the empirical performance of several experience replay techniques. Efficient experience replay plays an important role in model-free reinforcement learning by improving sample efficiency through reusing past experience. However, replay-based methods were largely...
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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...