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Skip to Search Results- 23reinforcement learning
- 4machine learning
- 3artificial intelligence
- 3planning
- 2continual learning
- 2deep learning
- 1Bennett, Brendan
- 1Carvalho, Tales Henrique
- 1Chan, Alan
- 1De Asis, Kris
- 1Guo, Yourui
- 1Holland, Gordon Z.
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Fall 2023
Learning only by direct interaction with the world can be expensive in many real world applications. In such settings, Model-based Reinforcement Learning (MBRL) methods are a promising avenue towards data-efficiency. By planning with a model, a sequential decision making agent can decrease its...
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Fall 2020
High stress levels and depression are commonly observed in hospitalized patients, which may negatively impact them on recovery after surgery. Sound therapy has been widely used for its effectiveness in increasing relaxation and reducing stress levels, and one could also fine-tune music features...
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Fall 2018
Dyna is an architecture for reinforcement learning agents that interleaves planning, acting, and learning in an online setting. This architecture aims to make fuller use of limited experience to achieve better performance with fewer environmental interactions. Dyna has been well studied in...