深度增强学习方向论文整理

本文为知乎专栏作者Alex-zhai原创，已授权优快云转载。
责编：王艺

一. 开山鼻祖DQN

1. Playing Atari with Deep Reinforcement Learning，V. Mnih et al., NIPS Workshop, 2013.

2. 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）

1. Dueling Network Architectures for Deep Reinforcement Learning. Z. Wang et al., arXiv, 2015.

2. Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.

3. Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.

4. Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.

5. Dynamic Frame skip Deep Q Network, A. S. Lakshminarayanan et al., IJCAI Deep RL Workshop, 2016.

6. Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.

7. How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.

8. Learning functions across many orders of magnitudes，H Van Hasselt，A Guez，M Hessel，D Silver

9. Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.

10. State of the Art Control of Atari Games using shallow reinforcement learning

11. Learning to Play in a Day: Faster Deep Reinforcement Learning by Optimality Tightening（11.13更新）

12. 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）

1. Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.

2. Deep Attention Recurrent Q-Network

3. Control of Memory, Active Perception, and Action in Minecraft, J. Oh et al., ICML, 2016.

4. Progressive Neural Networks

5. Language Understanding for Text-based Games Using Deep Reinforcement Learning

6. Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks

7. Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation

8. 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：

1. End-to-End Training of Deep Visuomotor Policies

2. Learning Deep Control Policies for Autonomous Aerial Vehicles with MPC-Guided Policy Search

3. Trust Region Policy Optimization

深度行动者评论家算法：

1. Deterministic Policy Gradient Algorithms

2. Continuous control with deep reinforcement learning

3. High-Dimensional Continuous Control Using Using Generalized Advantage Estimation