title: Maximum a Posteriori Policy Optimisation
abstract: We introduce a new algorithm for reinforcement learning called Maximum a-posteriori Policy Optimisation (MPO) based on coordinate ascent on a relative-entropy objective. We show that several existing methods can directly be related to our derivation. We develop two off-policy algorithms and demonstrate that they are competitive with the state-of-the-art in deep reinforcement learning. In particular, for continuous control, our method outperforms existing methods with respect to sample efficiency, premature convergence and robustness to hyperparameter settings.
authors: Abbas Abdolmaleki Jost Tobias Springenberg Yuval Tassa Remi Munos Nicolas Heess Martin Riedmiller
hIndex: Remi Munos 72