DDPG is a model-free actor-critic algorithm that learns continuous control policies end-to-end from states or pixels using deterministic policy gradients and deep networks, solving more than 20 physics tasks competitively with full-information planning methods.
Evolving deep unsupervised convolu- tional networks for vision-based reinforcement learning
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Continuous control with deep reinforcement learning
DDPG is a model-free actor-critic algorithm that learns continuous control policies end-to-end from states or pixels using deterministic policy gradients and deep networks, solving more than 20 physics tasks competitively with full-information planning methods.