Policy Search by Target Distribution Learning for Continuous Control

27 May 2019 Chuheng Zhang Yuanqi Li Jian Li

We observe that several existing policy gradient methods (such as vanilla policy gradient, PPO, A2C) may suffer from overly large gradients when the current policy is close to deterministic (even in some very simple environments), leading to an unstable training process. To address this issue, we propose a new method, called \emph{target distribution learning} (TDL), for policy improvement in reinforcement learning... (read more)

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Methods used in the Paper

Entropy Regularization
Policy Gradient Methods