drl:start

General Video Game Playing

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- Z. C. Lipton, J. Gao, L. Li, X. Li, F. Ahmed, and L. Deng, “Efficient Exploration for Dialogue Policy Learning with BBQ Networks & Replay Buffer Spiking,” arXiv:1608.05081 [cs, stat], Aug. 2016.
Adaptive Normalization
- H. van Hasselt et al., “Learning values across many orders of magnitude,” arXiv:1602.07714 [cs, stat], Feb. 2016.
Safe Off-Policy
- R. Munos et al., “Safe and Efficient Off-Policy Reinforcement Learning,” arXiv preprint arXiv:1606.02647, 2016.
Empowerment
- S. Mohamed and D. J. Rezende, “Variational information maximisation for intrinsically motivated reinforcement learning,” Advances in Neural Information Processing Systems, pp. 2125–2133, 2015.
Universal Values
- T. Schaul et al., “Universal value function approximators,” in Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015, pp. 1312–1320.
Macro-Actions
- A. Vezhnevets et al., “Strategic Attentive Writer for Learning Macro-Actions,” in Advances in Neural Information Processing Systems 29, D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, Eds. Curran Associates, Inc., 2016, pp. 3486–3494.
Successor Features
- A. Barreto et al., “Successor Features for Transfer in Reinforcement Learning,” arXiv preprint arXiv:1606.05312, 2016.
Progressive Network
- A. A. Rusu et al., “Progressive neural networks,” arXiv preprint arXiv:1606.04671, 2016.
2014-05, Deep AutoRegressive Networks

Deep Q-Learning	V. Mnih et al., “Human-level control through deep reinforcement learning,” Nature, vol. 518, no. 7540, pp. 529–533, Feb. 2015.
Double DQN	H. van Hasselt, A. Guez, and D. Silver, “Deep Reinforcement Learning with Double Q-learning,” arXiv:1509.06461 [cs], Sep. 2015.
DDPG	T. P. Lillicrap et al., “Continuous control with deep reinforcement learning,” arXiv:1509.02971 [cs, stat], Sep. 2015.
Async. Deep RL	V. Mnih et al., “Asynchronous Methods for Deep Reinforcement Learning,” arXiv:1602.01783 [cs], Feb. 2016.

Count & Exploration	I. Osband, C. Blundell, A. Pritzel, and B. Van Roy, “Deep Exploration via Bootstrapped DQN,” arXiv:1602.04621 [cs, stat], Feb. 2016.

Prioritized Replay	T. Schaul, J. Quan, I. Antonoglou, and D. Silver, “Prioritized Experience Replay,” arXiv:1511.05952 [cs], Nov. 2015.

Dueling Network	Z. Wang, N. de Freitas, and M. Lanctot, “Dueling network architectures for deep reinforcement learning,” arXiv preprint arXiv:1511.06581, 2015.

ViZDoom	M. Kempka, M. Wydmuch, G. Runc, J. Toczek, and W. Jaśkowski, “ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning,” arXiv:1605.02097 [cs], May 2016.
OpenAIGym	https://gym.openai.com/
Universe	https://universe.openai.com/
DeepMind Lab	https://deepmind.com/blog/open-sourcing-deepmind-lab/
Malmo	https://github.com/Microsoft/malmo

AlphaGo	D. Silver et al., “Mastering the game of Go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, Jan. 2016.
Anold	G. Lample and D. S. Chaplot, “Playing FPS Games with Deep Reinforcement Learning,” arXiv:1609.05521 [cs], Sep. 2016.