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Papers/Continuous control with deep reinforcement learning

Continuous control with deep reinforcement learning

Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra

2015-09-09Action DetectionReinforcement LearningContinuous ControlOpenAI GymSpeech Emotion RecognitionQ-Learning
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Abstract

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.

Results

TaskDatasetMetricValueModel
OpenAI GymHumanoid-v4Average Return139.14DDPG
OpenAI GymHalfCheetah-v4Average Return14934.86DDPG
OpenAI GymAnt-v4Average Return1712.12DDPG
OpenAI GymWalker2d-v4Average Return2994.54DDPG
OpenAI GymHopper-v4Average Return1290.24DDPG

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