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Skip to Search Results- 4Model-Based Reinforcement Learning
- 2Planning
- 2Reinforcement Learning
- 1Classification
- 1Classification Calibration
- 1Classification-Based Policy Iteration
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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,...
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Toward Practical Reinforcement Learning Algorithms: Classification Based Policy Iteration and Model-Based Learning
DownloadSpring 2017
In this dissertation, we advance the theoretical understanding of two families of Reinforcement Learning (RL) methods: Classification-based policy iteration (CBPI) and model-based reinforcement learning (MBRL) with factored semi-linear models. In contrast to generalized policy iteration, CBPI...
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Fall 2021
This thesis proposes novel algorithmic ideas in reinforcement learning for regret minimization. These algorithmic ideas enjoy nice theoretical guarantees and are more practical in large problems than their alternatives. We focus on finite-horizon episodic RL. We propose model-based and model-free...