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pro vyhledávání: '"Talvitie, Erin"'
Autor:
Talvitie, Erin J., Shao, Zilei, Li, Huiying, Hu, Jinghan, Boerma, Jacob, Zhao, Rory, Wang, Xintong
In model-based reinforcement learning, simulated experiences from the learned model are often treated as equivalent to experience from the real environment. However, when the model is inaccurate, it can catastrophically interfere with policy learning
Externí odkaz:
http://arxiv.org/abs/2406.16006
In model-based reinforcement learning, planning with an imperfect model of the environment has the potential to harm learning progress. But even when a model is imperfect, it may still contain information that is useful for planning. In this paper, w
Externí odkaz:
http://arxiv.org/abs/2007.02418
Dyna-style reinforcement learning (RL) agents improve sample efficiency over model-free RL agents by updating the value function with simulated experience generated by an environment model. However, it is often difficult to learn accurate models of e
Externí odkaz:
http://arxiv.org/abs/2006.04363
Dyna is a fundamental approach to model-based reinforcement learning (MBRL) that interleaves planning, acting, and learning in an online setting. In the most typical application of Dyna, the dynamics model is used to generate one-step transitions fro
Externí odkaz:
http://arxiv.org/abs/1806.01825
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