Open AccessProceedings Article
Continuous control with deep reinforcement learning
Timothy P. Lillicrap,Jonathan J. Hunt,Alexander Pritzel,Nicolas Heess,Tom Erez,Yuval Tassa,David Silver,Daan Wierstra +7 more
TLDR
In this paper, an actor-critic, model-free algorithm based on the deterministic policy gradient is proposed to operate over continuous action spaces, which 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.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.read more
Citations
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Deep Reinforcement Learning: A Brief Survey
TL;DR: Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higher-level understanding of the visual world as discussed by the authors.
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Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
Nguyen Cong Luong,Dinh Thai Hoang,Shimin Gong,Dusit Niyato,Ping Wang,Ying-Chang Liang,Dong In Kim +6 more
TL;DR: This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking, and presents applications of DRL for traffic routing, resource sharing, and data collection.
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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
TL;DR: In this article, a deep reinforcement learning algorithm based on off-policy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots.
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Deep Reinforcement Learning: An Overview
TL;DR: This work discusses core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration, and important mechanisms for RL, including attention and memory, unsupervised learning, transfer learning, multi-agent RL, hierarchical RL, and learning to learn.
Proceedings Article
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
Dmitry Kalashnikov,Alex Irpan,Peter Pastor,Julian Ibarz,Alexander Herzog,Eric Jang,Deirdre Quillen,Ethan Holly,Mrinal Kalakrishnan,Vincent Vanhoucke,Sergey Levine +10 more
TL;DR: QT-Opt as mentioned in this paper is a scalable self-supervised vision-based reinforcement learning framework that can leverage over 580k real-world grasp attempts to train a deep neural network Q-function with over 1.2M parameters.
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Human-level control through deep reinforcement learning
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Playing Atari with Deep Reinforcement Learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Alex Graves,Ioannis Antonoglou,Daan Wierstra,Martin Riedmiller +6 more
TL;DR: This work presents the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning, which outperforms all previous approaches on six of the games and surpasses a human expert on three of them.
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