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Complex sequential tasks in continuous-control settings often require agents to successfully traverse a set of "narrow passages" in their state space.
Learning to achieve goals
Leslie Pack Kaelbling · 1993
Earlier work this paper cites.
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Christopher G Atkeson and Stefan Schaal · 1997
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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.
Deterministic policy gradient algorithms
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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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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.
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
Earlier work this paper cites.
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