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About this paper
Yusheng Jiao, Feng Ling, Sina Heydari, Nicolas Heess, Josh Merel, Eva Kanso
The authors explain how to formulate and train embodied feedback controllers with model-free actor–critic reinforcement learning. They describe how an actor selects actions, a critic estimates expected reward, and an advantage compares action value with state value. They discuss choices in sensing, actions, rewards, and training. The paper presents derivations and guidance, not task comparisons or evidence.
Summary by alphaXiv.
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