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
Proceedings ArticleDOI
Hierarchical Reinforcement Learning Method for Autonomous Vehicle Behavior Planning
TL;DR: In this paper, a hierarchical reinforcement learning (HRL) based behavior planning structure is proposed to perform autonomous vehicle planning tasks in simulated environments with multiple sub-goals and a hybrid reward mechanism is designed for different hierarchical layers in the proposed HRL structure.
Journal ArticleDOI
The Actor-Dueling-Critic Method for Reinforcement Learning.
TL;DR: An approach based on the actor-critic framework is presented, and in the critic branch the manner of estimating Q-value is modified by introducing the advantage function, such as dueling network, which can estimate the action-advantage value.
Journal ArticleDOI
Has Artificial Intelligence Impacted Drug Discovery
TL;DR: In this article, the authors present a historical overview of the main advances in the field and highlight some of the recent applications of generative models in drug design with a focus into research work from the biopharmaceutical industry.
Proceedings ArticleDOI
Robot Navigation with Map-Based Deep Reinforcement Learning
TL;DR: In this paper, an end-to-end deep reinforcement learning approach for mobile robot navigation with dynamic obstacles avoidance is proposed using experience collected in a simulation environment, a convolutional neural network (CNN) is trained to predict proper steering actions of a robot from its egocentric local occupancy maps, which accommodate various sensors and fusion algorithms.
Proceedings ArticleDOI
An Experience Driven Design for IEEE 802.11ac Rate Adaptation based on Reinforcement Learning
TL;DR: In this article, the authors apply deep reinforcement learning (DRL) into designing a scalable, intelligent RA, designated as experience driven rate adaptation (EDRA), which enables the online learning capability of EDRA, which not only automatically identifies useful correlations between important factors and performance for the rate search, but also derives lowoverhead avenues to approach highest-goodput (HG) rates by learning from experience.
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