Built on

Richard Bellman. On the Theory of Dynamic Programming. In PNAS, 38(8):716-719, 1952
1952
Earlier work this paper cites.
Bentley, Jon Louis. Multidimensional binary search trees used for associative searching. Commun. ACM, 18(9): 509-517, 1975
1975
Earlier work this paper cites.
Long-Ji Lin. Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching. Machine Learning, 8(3-4):293-321,1992
1992
Earlier work this paper cites.
Christopher JCH Watkins and Peter Dayan. Q-Learning. Machine Learning, 8(3-4):279-292, 1992
1992
Earlier work this paper cites.
Jing Peng and Ronald Williams. Incremental multi-step Q-learning. Machine Learning, 22:283- 290, 1996
1996
Earlier work this paper cites.
Hado van Hasselt. Double Q-Learning. In NIPS, 2010
2010
Earlier work this paper cites.

Similar

Alex Krizhevsky, Ilya Sutskever, Geoffrey E,Hinton. ImageNet classification with deep convolutional neural networks. In ANIPS, 2012
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Then

Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. OpenAI Gym. arXiv:1606.01540, 2016
Original
2016
Later among the works it cites.
Hado van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver. Learning values across many orders of magnitudes. In NIPS, 2016
2016
Later among the works it cites.
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath. A Brief Survey of Deep Reinforcement Learning. arXiv:1708.05866, 2017
Original
2017
Later among the works it cites.
Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adria‘ Puigdome‘nech, Oriol Vinyals, Demis Hassabis, Daan Wierstra, and Charles Blundell. Neural Episodic Control. arXiv:1703.01988, 2017
Original
2017
Later among the works it cites.

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2018

Faster Deep Q-learning using Neural Episodic Control

Nishio, Daichi, Yamane, Satoshi

Understand

The research on deep reinforcement learning which estimates Q-value by deep learning has been attracted the interest of researchers recently.

In deep reinforcement learning, it is important to efficiently learn the experiences that an agent has collected by exploring environment.
We propose NEC2DQN that improves learning speed of a poor sample efficiency algorithm such as DQN by using good one such as NEC at the beginning of learning.
We show it is able to learn faster than Double DQN or N-step DQN in the experiments of Pong.

Built on

Richard Bellman. On the Theory of Dynamic Programming. In PNAS, 38(8):716-719, 1952
1952
Earlier work this paper cites.
Bentley, Jon Louis. Multidimensional binary search trees used for associative searching. Commun. ACM, 18(9): 509-517, 1975
1975
Earlier work this paper cites.
Long-Ji Lin. Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching. Machine Learning, 8(3-4):293-321,1992
1992
Earlier work this paper cites.
Christopher JCH Watkins and Peter Dayan. Q-Learning. Machine Learning, 8(3-4):279-292, 1992
1992
Earlier work this paper cites.
Jing Peng and Ronald Williams. Incremental multi-step Q-learning. Machine Learning, 22:283- 290, 1996
1996
Earlier work this paper cites.
Hado van Hasselt. Double Q-Learning. In NIPS, 2010
2010
Earlier work this paper cites.

Similar

Alex Krizhevsky, Ilya Sutskever, Geoffrey E,Hinton. ImageNet classification with deep convolutional neural networks. In ANIPS, 2012
2012
Cited alongside, same era.
Mnih, Volodymyr, Kavukcuoglu, Koray, Silver, David, Rusu, Andrei A, Veness, Joel, Bellemare, Marc G, Graves, Alex, Riedmiller, Martin, Fidjeland, Andreas K, Ostrovski, Georg, et al. Human-level control through deep reinforcement learning. In Nature, 518(7540):529-533, 2015
2015
Cited alongside, same era.
Schaul, Tom Schaul, John Quan, Ioannis Antonoglou, and David Silver. Prioritized Experience Replay. In ICLR, 2016
2016
Cited alongside, same era.
Hado van Hasselt, Arthur Guez, and David Silver. Deep Reinforcement Learning with Double Q-Learning. In AAAI, 2016
2016
Cited alongside, same era.
Charles Blundell, Benigno Uria, Alexander Pritzel, Yazhe Li, Avraham Ruderman, Joel Z Leibo, Jack Rae, Daan Wierstra, and Demis Hassabis. Model-Free Episodic Control. arXiv:1606.04460, 2016

Then

Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. OpenAI Gym. arXiv:1606.01540, 2016
Original
2016
Later among the works it cites.
Hado van Hasselt, Arthur Guez, Matteo Hessel, Volodymyr Mnih, David Silver. Learning values across many orders of magnitudes. In NIPS, 2016
2016
Later among the works it cites.
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, and Anil Anthony Bharath. A Brief Survey of Deep Reinforcement Learning. arXiv:1708.05866, 2017
Original
2017
Later among the works it cites.
Alexander Pritzel, Benigno Uria, Sriram Srinivasan, Adria‘ Puigdome‘nech, Oriol Vinyals, Demis Hassabis, Daan Wierstra, and Charles Blundell. Neural Episodic Control. arXiv:1703.01988, 2017
Original
2017
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2016

Cited alongside, same era.

Remi Munos, Tom Stepleton, Anna Harutyunyan, and Marc G Bellemare. Safe and Efficient Off-Policy Reinforcement Learning. In NIPS, 2016

2016

Cited alongside, same era.

Shoya Matsumori, Takuma Seno, Toshiki Kikuchi, Yusuke Takimoto, Masahiko Osawa, Michita Imai. Embedding Cognitive Map in Neural Episodic Control. In SIG-AGI, 2017

2017

Later among the works it cites.

Todd Hester, Matej Vecerik, Olivier Pietquin, Marc Lanctot, Tom Schaul, Bilal Piot, Andrew Sendonaris, Gabriel Dulac-Arnold, Ian Osband, John Agapiou, et al. Deep Q-learning from Demonstrations. arXiv:1704.03732, 2017

Original

2017

Later among the works it cites.