Open AccessProceedings Article
Prioritized Experience Replay
TLDR
Prioritized experience replay as mentioned in this paper is a framework for prioritizing experience, so as to replay important transitions more frequently, and therefore learn more efficiently, achieving human-level performance across many Atari games.Abstract:
Experience replay lets online reinforcement learning agents remember and reuse experiences from the past. In prior work, experience transitions were uniformly sampled from a replay memory. However, this approach simply replays transitions at the same frequency that they were originally experienced, regardless of their significance. In this paper we develop a framework for prioritizing experience, so as to replay important transitions more frequently, and therefore learn more efficiently. We use prioritized experience replay in Deep Q-Networks (DQN), a reinforcement learning algorithm that achieved human-level performance across many Atari games. DQN with prioritized experience replay achieves a new state-of-the-art, outperforming DQN with uniform replay on 41 out of 49 games.read more
Citations
More filters
Journal ArticleDOI
Cognitive Optimal-Setting Control of AIoT Industrial Applications With Deep Reinforcement Learning
TL;DR: An expected advantage learning method is proposed for moderating the maximum value of expectation-based deep reinforcement learning for industrial applications by replacing the sigmod function with the tanh function as the softmax function activation value and decreasing the value overfitting in cognitive computing.
Journal ArticleDOI
Leveraging Task Modularity in Reinforcement Learning for Adaptable Industry 4.0 Automation
TL;DR: A meta reinforcement learning framework is proposed to enhance the adaptability of collaborative robots to new tasks through task modularization and efficient transfer of policies from previously learned task modules to investigate the current limitations of learning robots in industrial settings.
Posted Content
Learning to Sketch with Deep Q Networks and Demonstrated Strokes.
Tao Zhou,Chen Fang,Zhaowen Wang,Jimei Yang,Byungmoon Kim,Zhili Chen,Jonathan Brandt,Demetri Terzopoulos +7 more
TL;DR: A two-stage learning framework to teach a machine to doodle in a simulated painting environment via Stroke Demonstration and deep Q-learning (SDQ), which generates a sequence of pen actions to reproduce a reference drawing and mimics the behavior of human painters.
Journal ArticleDOI
Learning Barrier Functions With Memory for Robust Safe Navigation
TL;DR: In this paper, the authors used on-board range sensing to construct a robust control barrier safety constraint, which takes into account the error in the estimated distance fields and its gradient.
Posted Content
Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic
TL;DR: This work proposes a combination of reinforcement learning and game theory to learn merging behaviors, and designs a training curriculum for a reinforcement learning agent using the concept of level-k behavior.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
Posted Content
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.
Proceedings ArticleDOI
A discriminatively trained, multiscale, deformable part model
TL;DR: A discriminatively trained, multiscale, deformable part model for object detection, which achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge and outperforms the best results in the 2007 challenge in ten out of twenty categories.
Related Papers (5)
Human-level control through deep reinforcement learning
Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more