High-Dimensional Continuous Control Using Generalized Advantage Estimation

8 Jun 2015John SchulmanPhilipp MoritzSergey LevineMichael JordanPieter Abbeel

Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear function approximators such as neural networks. The two main challenges are the large number of samples typically required, and the difficulty of obtaining stable and steady improvement despite the nonstationarity of the incoming data... (read more)

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