DeepMind papers at ICLR 2018 DeepMind

Here you can read details of all DeepMind's accepted papers and find out where you can see the accompanying poster sessions and talks. 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.