10.1k citations · 10.9k across the 10 of their papers we have counts for
3 papers · 1 filter
Memory-based control with recurrent neural networks
Nicolas Heess, Jonathan J Hunt, Timothy P Lillicrap +1
Partially observed control problems are a challenging aspect of reinforcement learning. We extend two related, model-free algorithms for continuous control -- deterministic policy…
Deep Reinforcement Learning in Large Discrete Action Spaces
Gabriel Dulac-Arnold, Richard Evans, Hado van Hasselt +7
Being able to reason in an environment with a large number of discrete actions is essential to bringing reinforcement learning to a larger class of problems. Recommender systems, i…
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…