深度增强学习方向论文整理_语言 & 开发_Alex-zhai

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）
Safe and Efficient Off-Policy Reinforcement Learning（12.20 更新）
The Predictron: End-To-End Learning and Planning （1.3 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
Value Iteration Networks, NIPS, 2016 （12.20 更新）
MazeBase：A sandbox for learning from games（12.20 更新）
Strategic Attentive Writer for Learning Macro-Actions（12.20 更新）

四. 基于策略梯度的深度强化学习

深度策略梯度：

End-to-End Training 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 Algorithms
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）

搜索与监督：

End-to-End Training 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 Estimation 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
Generalizing Skills with Semi-Supervised Reinforcement Learning（12.20 更新）

五. 分层 DRL

Deep Successor 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）

六. DRL 中的多任务和迁移学习

ADAAPT: A Deep Architecture 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 of Memory, Active Perception, and Action in Minecraft
Model-Free Episodic Control

八. DRL 中探索与利用问题

Action-Conditional 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）
VIME: Variational Information Maximizing Exploration（12.20 更新）

九. 多 Agent 的 DRL

Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks
Multiagent Cooperation and Competition with Deep Reinforcement Learning

十. 逆向 DRL

Guided Cost 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）

十一. 探索+监督学习

Deep learning for real-time Atari game play using offline Monte-Carlo tree search planning
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.

十二. 异步 DRL

Asynchronous Methods for Deep Reinforcement Learning
Reinforcement 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 Distillation
Universal Value Function Approximators
Learning values across many orders of magnitude

十五：德州 poker

Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
Fictitious Self-Play in Extensive-Form Games
Smooth UCT search in computer poker

十六：Doom 游戏

ViZDoom: A 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）

十七：大规模动作空间

Deep Reinforcement Learning in Large Discrete Action Spaces

十八：参数化连续动作空间

Deep Reinforcement Learning in Parameterized Action Space

十九：Deep Model

Learning Visual Predictive Models of Physics for Playing Billiards
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

4.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 Optimization
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

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-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning

自动调控参数：

Using Deep Q-Learning to Control Optimization Hyperparameters

人机对话：

Deep Reinforcement 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

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