Leveraging deep generative models for non-parametric distributions in reinforcement learning
This thesis explores the use of deep generative models to enhance distribution representations in reinforcement learning (RL), leading to improved exploration, stability, and performance. It focuses on two roles of distributions in RL: policy distributions and action distributions. For policy distri...
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| Format: | Thesis-Doctor of Philosophy |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/173455 |