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- 1Artificial Intelligence
- 1Exploration
- 1Model-Based Reinforcement Learning
- 1Planning
- 1Reinforcement Learning
- 1Reinforcement learning
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Spring 2020
Reinforcement Learning is a formalism for learning by trial and error. Unfortunately, trial and error can take a long time to find a solution if the agent does not efficiently explore the behaviours available to it. Moreover, how an agent ought to explore depends on the task that the agent is...
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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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