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