Optimal planning with approximate model-based reinforcement learning
Model-based reinforcement learning methods make efficient use of samples by building a model of the environment and planning with it. Compared to model-free methods, they usually take fewer samples to converge to the optimal policy. Despite that efficiency, model-based methods may not learn the opti...
| Main Author: | |
|---|---|
| Language: | English |
| Published: |
University of British Columbia
2012
|
| Online Access: | http://hdl.handle.net/2429/39889 |
Search Result 1
Search Result 2