Continuous control with deep reinforcement learning
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 gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing up, dexterous manipulation, legged locomotion and car driving. Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies end to end: directly from raw pixel inputs.
ICLR