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Balancing exploration and exploitation is a central goal in reinforcement learning (RL).
Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, Anind K Dey, et al · 2008
Earlier work this paper cites.
High-dimensional continuous control using generalized advantage estimation
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Earlier work this paper cites.
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Earlier work this paper cites.
Randomized exploration for reinforcement learning with general value function approximation
Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Yang Zhuoran, Zhaoran Wang, Doina Precup, and F. Lin Yang · 2021
Earlier work this paper cites.
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