An Advisor-Based Architecture for a Sample-Efficient Training of Autonomous Navigation Agents with Reinforcement Learning
Recent advancements in artificial intelligence have enabled reinforcement learning (RL) agents to exceed human-level performance in various gaming tasks. However, despite the state-of-the-art performance demonstrated by model-free RL algorithms, they suffer from high sample complexity. Hence, it is...
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| Autors principals: | , , , , , |
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| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
MDPI AG
2023-09-01
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| Col·lecció: | Robotics |
| Matèries: | |
| Accés en línia: | https://www.mdpi.com/2218-6581/12/5/133 |
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