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2023
In classic reinforcement learning(RL) for continuous control, agents make decisions at discrete and fixed time intervals. The duration between decisions becomes a crucial hyperparameter. Setting it too short may increase the problem’s difficulty by requiring the agent to make numerous decisions to achieve its goal, while setting it too long can result ...
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In classic reinforcement learning(RL) for continuous control, agents make decisions at discrete and fixed time intervals. The duration between decisions becomes a crucial hyperparameter. Setting it too short may increase the problem’s difficulty by requiring the agent to make numerous decisions to achieve its goal, while setting it too long can result ...
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AIAA Infotech@Aerospace Conference, 2009
We examine the complementary strengths of value function based policy learning and guided search. Our work unifies rollout based open-loop feedback control (outlined by Bertsekas 2 ) and plan-space approximate dynamic programming (studied by Boyan 3 ). We exploit the strengths of this unified space by finding a natural order and metric for considering ...
Lawrence Bush +2 more
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We examine the complementary strengths of value function based policy learning and guided search. Our work unifies rollout based open-loop feedback control (outlined by Bertsekas 2 ) and plan-space approximate dynamic programming (studied by Boyan 3 ). We exploit the strengths of this unified space by finding a natural order and metric for considering ...
Lawrence Bush +2 more
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Closed and Open Loop Oil Taxation Policies in New Mexico
SSRN Electronic Journal, 2023Saeed Langarudi +2 more
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Kyoto and Beyond Kyoto Climate Policy: Comparison of Open-Loop and Feedback Game Outcomes
2004Juan Carlos Císcar, Antonio Soria
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2025 13th International Conference on Traffic and Logistic Engineering (ICTLE)
Wenyan Pei, Yipeng Li
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Wenyan Pei, Yipeng Li
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Economic performance of pyrolysis of mixed plastic waste: Open-loop versus closed-loop recycling
Journal of Cleaner Production, 2020Macarena Larrain +2 more
exaly

