Deterministic Policy Gradient Algorithms

  title={Deterministic Policy Gradient Algorithms},
  author={David Silver and Guy Lever and Nicolas Heess and Thomas Degris and Daan Wierstra and Martin A. Riedmiller},
In this paper we consider deterministic policy gradient algorithms for reinforcement learning with continuous actions. The deterministic policy gradient has a particularly appealing form: it is the expected gradient of the action-value function. This simple form means that the deterministic policy gradient can be estimated much more efficiently than the usual stochastic policy gradient. To ensure adequate exploration, we introduce an off-policy actor-critic algorithm that learns a deterministic… CONTINUE READING
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