Algorithms include:
N-step DQN (introduced in paper by Richard Sutton ([2] Sutton, 1988)
Double DQN (introduced in paper Deep Reinforcement Learning with Double Q-Learning ([3] van Hasselt, Guez, and Silver, 2015)
Noisy networks (introduced in paper Noisy Networks for Exploration ([4] Fortunato and others, 2017)
Prioritized buffer (introduced in paper Prioritized Experience Reply ([7] Schaul and otherse, 2015)
Dueling DQN (introduced in paper Dueling Network Architectures for Deep Reinforcement Learing ([8] Wang et al., 2015)
Categorical DQN (introduce in paper A Distributional Perspective on Reinforcement Learning ([9] Bellemare, Dabney and Munos 2017)
Combination Improvmeents (introduced in paper [1] Rainbow: Combining Improvements in Deep Reinforcement Learning)
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver, 2017, Rainbow: Combining Improvements in Deep Reinforcement Learning. arXiv:1710.02298
Sutton, R.S. 1988, Learning to Predict by the Methods of Temporal Differences, Machine Learning 3(1):9-44
Hado Van Hasselt, Arthur Guez, David Silver, 2015, Deep Reinforcement Learning with Double Q-Learning. arXiv:1509.06461v3
Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Pilot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Remi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg, 2017, Noisy Networks for Exploration arXiv:1706.10295v1
Marc Bellemare, Sriram Srinivasan, Georg Ostrovski, Tom Schaus, David Saxton, Remi Munos 2016, Unifying Count-Based Exploration and Intrinsic Motivation arXiv:1606.01868v2
Jarryd Martin, Suraj Narayanan Sasikumar, Tom Everitt, Marcus Hutter, 2017, Count-Based Exploration in Feature Space for Reinforcement Learning arXiv:1706.08090 Tom Schaul, John Quan, Ioannis Antonoglou, David Silver, 2015, Prioritized Experience Replay arXiv:1511.05952
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas, 2015, Dueling Network Architectures for Deep Reinforcement Learning arXiv:1511.06581
Marc G. Bellemare, Will Dabney, Rémi Munos, 2017, A Distributional Perspective on Reinforcement Learning arXiv:1707.06887