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We propose a new sample-efficient methodology, called Supervised Policy Update (SPU), for deep reinforcement learning.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
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Yuhuai Wu, Elman Mansimov, Roger B Grosse, Shun Liao, and Jimmy Ba · 2017
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Maximum a posteriori policy optimisation
Yuval Tassa Remi Munos Nicolas Heess Martin Riedmiller Abbas Abdolmaleki, Jost Tobias Springenberg · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
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