深度增强学习方向论文整理
2016-11-28 16:31
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本文为知乎专栏作者Alex-zhai原创,已授权CSDN转载。
责编:王艺
Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
Dynamic Frame skip Deep Q Network, A. S. Lakshminarayanan et al., IJCAI Deep RL Workshop, 2016.
Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
Learning functions across many orders of magnitudes,H Van Hasselt,A Guez,M Hessel,D Silver
Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
State
of the Art Control of Atari Games using shallow reinforcement learning
Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening(11.13更新)
Deep Reinforcement Learning with Averaged Target DQN(11.14更新)
Deep Attention
Recurrent Q-Network
Control of Memory, Active Perception, and Action in Minecraft, J. Oh et al., ICML, 2016.
Progressive
Neural Networks
Language Understanding
for Text-based Games Using Deep Reinforcement Learning
Learning to
Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks
Hierarchical
Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Recurrent Reinforcement Learning: A Hybrid Approach
Training of Deep Visuomotor Policies
Learning
Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search
Trust Region
Policy Optimization
Continuous
control with deep reinforcement learning
High-Dimensional
Continuous Control Using Using Generalized Advantage Estimation
Compatible
Value Gradients for Reinforcement Learning of Continuous Deep Policies
Deep Reinforcement
Learning in Parameterized Action Space
Memory-based
control with recurrent neural networks
Terrain-adaptive
locomotion skills using deep reinforcement learning
Compatible
Value Gradients for Reinforcement Learning of Continuous Deep Policies
SAMPLE EFFICIENT ACTOR-CRITIC WITH EXPERIENCE REPLAY(11.13更新)
Training of Deep Visuomotor Policies
Interactive Control of Diverse Complex Characters with Neural Networks
PGQ: COMBINING POLICY GRADIENT AND Q-LEARNING(11.13更新)
Using Stochastic Computation Graphs
Continuous
Deep Q-Learning with Model-based Acceleration
Benchmarking
Deep Reinforcement Learning for Continuous Control
Learning
Continuous Control Policies by Stochastic Value Gradients
Reinforcement Learning
Hierarchical
Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Hierarchical
Reinforcement Learning using Spatio-Temporal Abstractions and Deep Neural Networks
Stochastic Neural Networks for Hierarchical Reinforcement Learning – Authors: Carlos Florensa, Yan Duan, Pieter Abbeel (11.14更新)
Arc hitecture for Adaptive Policy Transfer from Multiple Sources
A Deep Hierarchical
Approach to Lifelong Learning in Minecraft
Actor-Mimic:
Deep Multitask and Transfer Reinforcement Learning
Policy Distillation
Progressive
Neural Networks
Universal Value Function Approximators
Multi-task learning with deep model based reinforcement learning(11.14更新)
Modular Multitask Reinforcement Learning with Policy Sketches (11.14更新)
Memory, Active Perception, and Action in Minecraft
Model-Free Episodic
Control
Video Prediction using Deep Networks in Atari Games
Curiosity-driven
Exploration in Deep Reinforcement Learning via Bayesian Neural Networks
Deep Exploration
via Bootstrapped DQN
Hierarchical
Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
Unifying Count-Based Exploration and Intrinsic Motivation
#Exploration: A Study of Count-Based Exploration for Deep Reinforcemen Learning(11.14更新)
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning(11.14更新)
Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks
Multiagent
Cooperation and Competition with Deep Reinforcement Learning
Learning: Deep Inverse Optimal Control via Policy Optimization
Maximum Entropy
Deep Inverse Reinforcement Learning
Generalizing Skills with Semi-Supervised Reinforcement Learning(11.14更新)
Better Computer Go Player with Neural Network and Long-term Prediction
Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU(11.14更新)
Strategic Attentive
Writer for Learning Macro-Actions
Unifying Count-Based Exploration and Intrinsic Motivation
Universal Value Function Approximators
Learning values
across many orders of magnitude
Fictitious Self-Play in Extensive-Form Games
Smooth UCT search in computer poker
Doom-based AI Research Platform for Visual Reinforcement Learning
Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning
Playing FPS
Games with Deep Reinforcement Learning
LEARNING TO ACT BY PREDICTING THE FUTURE(11.13更新)
Deep Reinforcement Learning From Raw Pixels in Doom(11.14更新)
Learning in Large Discrete Action Spaces
Learning in Parameterized Action Space
J. Schmidhuber, On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models, arXiv, 2015. arXiv
Learning Continuous Control Policies by Stochastic Value Gradients
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models
Action-Conditional Video Prediction using Deep Networks in Atari Games
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
Towards Vision-Based
Deep Reinforcement Learning for Robotic Motion Control
Path Integral
Guided Policy Search
Memory-based
control with recurrent neural networks
Learning Hand-Eye
Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
Learning Deep
Neural Network Policies with Continuous Memory States
High-Dimensional
Continuous Control Using Generalized Advantage Estimation
Guided Cost
Learning: Deep Inverse Optimal Control via Policy Optimization
End-to-End
Training of Deep Visuomotor Policies
DeepMPC: Learning Deep Latent Features for Model Predictive Control
Deep Visual
Foresight for Planning Robot Motion
Deep Reinforcement
Learning for Robotic Manipulation
Continuous
Deep Q-Learning with Model-based Acceleration
Collective Robot
Reinforcement Learning with Distributed Asynchronous Guided Policy Search
Asynchronous Methods for Deep Reinforcement Learning
Learning
Continuous Control Policies by Stochastic Value Gradients
Visual Navigation in Indoor Scenes using Deep Reinforcement Learning
Q-Learning to Control Optimization Hyperparameters
Learning for Dialogue Generation
SimpleDS: A
Simple Deep Reinforcement Learning Dialogue System
Strategic Dialogue
Management via Deep Reinforcement Learning
Towards End-to-End
Learning for Dialog State Tracking and Management using Deep Reinforcement Learning
Video Prediction using Deep Networks in Atari Games
Generative Model for Raw Audio
Text with Deep Reinforcement Learning
for Text-based Games Using Deep Reinforcement Learning
Learning Radio Control and Signal Detection with KeRLym, a Gym RL Agent
Tuning Recurrent Neural Networks with Reinforcement Learning(11.14更新)
Neural Architecture Search with Reinforcement Learning(11.14更新)
责编:王艺
一. 开山鼻祖DQN
Playing Atari with Deep Reinforcement Learning,V. Mnih et al., NIPS Workshop, 2013.Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
二. DQN的各种改进版本(侧重于算法上的改进)
Dueling Network Architectures for Deep Reinforcement Learning. Z. Wang et al., arXiv, 2015.Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
Dynamic Frame skip Deep Q Network, A. S. Lakshminarayanan et al., IJCAI Deep RL Workshop, 2016.
Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
Learning functions across many orders of magnitudes,H Van Hasselt,A Guez,M Hessel,D Silver
Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
State
of the Art Control of Atari Games using shallow reinforcement learning
Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening(11.13更新)
Deep Reinforcement Learning with Averaged Target DQN(11.14更新)
三. DQN的各种改进版本(侧重于模型的改进)
Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.Deep Attention
Recurrent Q-Network
Control of Memory, Active Perception, and Action in Minecraft, J. Oh et al., ICML, 2016.
Progressive
Neural Networks
Language Understanding
for Text-based Games Using Deep Reinforcement Learning
Learning to
Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks
Hierarchical
Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Recurrent Reinforcement Learning: A Hybrid Approach
四. 基于策略梯度的深度强化学习
深度策略梯度:
End-to-EndTraining of Deep Visuomotor Policies
Learning
Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search
Trust Region
Policy Optimization
深度行动者评论家算法:
Deterministic Policy Gradient AlgorithmsContinuous
control with deep reinforcement learning
High-Dimensional
Continuous Control Using Using Generalized Advantage Estimation
Compatible
Value Gradients for Reinforcement Learning of Continuous Deep Policies
Deep Reinforcement
Learning in Parameterized Action Space
Memory-based
control with recurrent neural networks
Terrain-adaptive
locomotion skills using deep reinforcement learning
Compatible
Value Gradients for Reinforcement Learning of Continuous Deep Policies
SAMPLE EFFICIENT ACTOR-CRITIC WITH EXPERIENCE REPLAY(11.13更新)
搜索与监督:
End-to-EndTraining of Deep Visuomotor Policies
Interactive Control of Diverse Complex Characters with Neural Networks
连续动作空间下探索改进:
Curiosity-driven Exploration in DRL via Bayesian Neuarl Networks结合策略梯度和Q学习:
Q-PROP: SAMPLE-EFFICIENT POLICY GRADIENT WITH AN OFF-POLICY CRITIC(11.13更新)PGQ: COMBINING POLICY GRADIENT AND Q-LEARNING(11.13更新)
其它策略梯度文章:
Gradient EstimationUsing Stochastic Computation Graphs
Continuous
Deep Q-Learning with Model-based Acceleration
Benchmarking
Deep Reinforcement Learning for Continuous Control
Learning
Continuous Control Policies by Stochastic Value Gradients
五. 分层DRL
Deep SuccessorReinforcement Learning
Hierarchical
Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Hierarchical
Reinforcement Learning using Spatio-Temporal Abstractions and Deep Neural Networks
Stochastic Neural Networks for Hierarchical Reinforcement Learning – Authors: Carlos Florensa, Yan Duan, Pieter Abbeel (11.14更新)
六. DRL中的多任务和迁移学习
ADAAPT: A DeepArc hitecture for Adaptive Policy Transfer from Multiple Sources
A Deep Hierarchical
Approach to Lifelong Learning in Minecraft
Actor-Mimic:
Deep Multitask and Transfer Reinforcement Learning
Policy Distillation
Progressive
Neural Networks
Universal Value Function Approximators
Multi-task learning with deep model based reinforcement learning(11.14更新)
Modular Multitask Reinforcement Learning with Policy Sketches (11.14更新)
七. 基于外部记忆模块的DRL模型
Control ofMemory, Active Perception, and Action in Minecraft
Model-Free Episodic
Control
八. DRL中探索与利用问题
Action-ConditionalVideo Prediction using Deep Networks in Atari Games
Curiosity-driven
Exploration in Deep Reinforcement Learning via Bayesian Neural Networks
Deep Exploration
via Bootstrapped DQN
Hierarchical
Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
Unifying Count-Based Exploration and Intrinsic Motivation
#Exploration: A Study of Count-Based Exploration for Deep Reinforcemen Learning(11.14更新)
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning(11.14更新)
九. 多Agent的DRL
Learning toCommunicate to Solve Riddles with Deep Distributed Recurrent Q-Networks
Multiagent
Cooperation and Competition with Deep Reinforcement Learning
十. 逆向DRL
Guided CostLearning: Deep Inverse Optimal Control via Policy Optimization
Maximum Entropy
Deep Inverse Reinforcement Learning
Generalizing Skills with Semi-Supervised Reinforcement Learning(11.14更新)
十一. 探索+监督学习
Deep learning for real-time Atari game play using offline Monte-Carlo tree search planningBetter Computer Go Player with Neural Network and Long-term Prediction
Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
十二. 异步DRL
Asynchronous Methods for Deep Reinforcement LearningReinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU(11.14更新)
十三:适用于难度较大的游戏场景
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation, T. D. Kulkarni et al., arXiv, 2016.Strategic Attentive
Writer for Learning Macro-Actions
Unifying Count-Based Exploration and Intrinsic Motivation
十四:单个网络玩多个游戏
Policy DistillationUniversal Value Function Approximators
Learning values
across many orders of magnitude
十五:德州poker
Deep Reinforcement Learning from Self-Play in Imperfect-Information GamesFictitious Self-Play in Extensive-Form Games
Smooth UCT search in computer poker
十六:Doom游戏
ViZDoom: ADoom-based AI Research Platform for Visual Reinforcement Learning
Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning
Playing FPS
Games with Deep Reinforcement Learning
LEARNING TO ACT BY PREDICTING THE FUTURE(11.13更新)
Deep Reinforcement Learning From Raw Pixels in Doom(11.14更新)
十七:大规模动作空间
Deep ReinforcementLearning in Large Discrete Action Spaces
十八:参数化连续动作空间
Deep ReinforcementLearning in Parameterized Action Space
十九:Deep Model
Learning Visual Predictive Models of Physics for Playing BilliardsJ. Schmidhuber, On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models, arXiv, 2015. arXiv
Learning Continuous Control Policies by Stochastic Value Gradients
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models
Action-Conditional Video Prediction using Deep Networks in Atari Games
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
二十:DRL应用
机器人领域:
Trust Region Policy OptimizationTowards Vision-Based
Deep Reinforcement Learning for Robotic Motion Control
Path Integral
Guided Policy Search
Memory-based
control with recurrent neural networks
Learning Hand-Eye
Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
Learning Deep
Neural Network Policies with Continuous Memory States
High-Dimensional
Continuous Control Using Generalized Advantage Estimation
Guided Cost
Learning: Deep Inverse Optimal Control via Policy Optimization
End-to-End
Training of Deep Visuomotor Policies
DeepMPC: Learning Deep Latent Features for Model Predictive Control
Deep Visual
Foresight for Planning Robot Motion
Deep Reinforcement
Learning for Robotic Manipulation
Continuous
Deep Q-Learning with Model-based Acceleration
Collective Robot
Reinforcement Learning with Distributed Asynchronous Guided Policy Search
Asynchronous Methods for Deep Reinforcement Learning
Learning
Continuous Control Policies by Stochastic Value Gradients
机器翻译:
Simultaneous Machine Translation using Deep Reinforcement Learning目标定位:
Active Object Localization with Deep Reinforcement Learning目标驱动的视觉导航:
Target-drivenVisual Navigation in Indoor Scenes using Deep Reinforcement Learning
自动调控参数:
Using DeepQ-Learning to Control Optimization Hyperparameters
人机对话:
Deep ReinforcementLearning for Dialogue Generation
SimpleDS: A
Simple Deep Reinforcement Learning Dialogue System
Strategic Dialogue
Management via Deep Reinforcement Learning
Towards End-to-End
Learning for Dialog State Tracking and Management using Deep Reinforcement Learning
视频预测:
Action-ConditionalVideo Prediction using Deep Networks in Atari Games
文本到语音:
WaveNet: AGenerative Model for Raw Audio
文本生成:
GeneratingText with Deep Reinforcement Learning
文本游戏:
Language Understandingfor Text-based Games Using Deep Reinforcement Learning
无线电操控和信号监控:
Deep ReinforcementLearning Radio Control and Signal Detection with KeRLym, a Gym RL Agent
DRL来学习做物理实验:
LEARNING TO PERFORM PHYSICS EXPERIMENTS VIA DEEP REINFORCEMENT LEARNING(11.13更新)DRL加速收敛:
Deep Reinforcement Learning for Accelerating the Convergence Rate(11.14更新)利用DRL来设计神经网络:
Designing Neural Network Architectures using Reinforcement Learning(11.14更新)Tuning Recurrent Neural Networks with Reinforcement Learning(11.14更新)
Neural Architecture Search with Reinforcement Learning(11.14更新)
控制信号灯:
Using a Deep Reinforcement Learning Agent for Traffic Signal Control(11.14更新)二十一:其它方向
避免危险状态:
Combating Deep Reinforcement Learning’s Sisyphean Curse with Intrinsic Fear (11.14更新)DRL中On-Policy vs. Off-Policy 比较:
On-Policy vs. Off-Policy Updates for Deep Reinforcement Learning(11.14更新)相关文章推荐
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