10.1k citations · 13.3k across the 33 of their papers we have counts for
7 papers · 1 filter
Learning Dynamics Models for Model Predictive Agents
Michael Lutter, Leonard Hasenclever, Arunkumar Byravan +5
Model-Based Reinforcement Learning involves learning a \textit{dynamics model} from data, and then using this model to optimise behaviour, most often with an online \textit{planner…
Local Search for Policy Iteration in Continuous Control
Jost Tobias Springenberg, Nicolas Heess, Daniel Mankowitz +10
We present an algorithm for local, regularized, policy improvement in reinforcement learning (RL) that allows us to formulate model-based and model-free variants in a single framew…
Relative Entropy Regularized Policy Iteration
Abbas Abdolmaleki, Jost Tobias Springenberg, Jonas Degrave +5
We present an off-policy actor-critic algorithm for Reinforcement Learning (RL) that combines ideas from gradient-free optimization via stochastic search with learned action-value…
Maximum a Posteriori Policy Optimisation
Abbas Abdolmaleki, Jost Tobias Springenberg, Yuval Tassa +3
We introduce a new algorithm for reinforcement learning called Maximum aposteriori Policy Optimisation (MPO) based on coordinate ascent on a relative entropy objective. We show tha…
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation
Ivaylo Popov, Nicolas Heess, Timothy Lillicrap +7
Deep learning and reinforcement learning methods have recently been used to solve a variety of problems in continuous control domains. An obvious application of these techniques is…
Learning Continuous Control Policies by Stochastic Value Gradients
Nicolas Heess, Greg Wayne, David Silver +3
We present a unified framework for learning continuous control policies using backpropagation. It supports stochastic control by treating stochasticity in the Bellman equation as a…
Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel +5
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gra…