Deep Reinforcement Learning in Parameterized Action Space

13 Nov 2015Matthew HausknechtPeter Stone

Recent work has shown that deep neural networks are capable of approximating both value functions and policies in reinforcement learning domains featuring continuous state and action spaces. However, to the best of our knowledge no previous work has succeeded at using deep neural networks in structured (parameterized) continuous action spaces... (read more)

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