Continuous control with deep reinforcement learning
Abstrak
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.
Topik & Kata Kunci
Penulis (8)
T. Lillicrap
Jonathan J. Hunt
A. Pritzel
N. Heess
Tom Erez
Yuval Tassa
David Silver
Daan Wierstra
Akses Cepat
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- 2015
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