publication . Preprint . 2016

Playing SNES in the Retro Learning Environment

Bhonker, Nadav; Rozenberg, Shai; Hubara, Itay;
Open Access English
  • Published: 07 Nov 2016
Abstract
Mastering a video game requires skill, tactics and strategy. While these attributes may be acquired naturally by human players, teaching them to a computer program is a far more challenging task. In recent years, extensive research was carried out in the field of reinforcement learning and numerous algorithms were introduced, aiming to learn how to perform human tasks such as playing video games. As a result, the Arcade Learning Environment (ALE) (Bellemare et al., 2013) has become a commonly used benchmark environment allowing algorithms to train on various Atari 2600 games. In many games the state-of-the-art algorithms outperform humans. In this paper we intro...
Subjects
ACM Computing Classification System: ComputingMilieux_PERSONALCOMPUTING
free text keywords: Computer Science - Learning, Computer Science - Artificial Intelligence
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19 references, page 1 of 2

Libretro. www.libretro.com. URL www.libretro.com. Accessed: 2016-11-03.

M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling. The arcade learning environment: An evaluation platform for general agents. Journal of Artificial Intelligence Research, 47:253-279, jun 2013. [OpenAIRE]

B. Bischoff, D. Nguyen-Tuong, I.-H. Lee, F. Streichert, and A. Knoll. Hierarchical reinforcement learning for robot navigation. In ESANN, 2013. [OpenAIRE]

G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba. Openai gym. arXiv preprint arXiv:1606.01540, 2016.

M. Campbell, A. J. Hoane, and F.-h. Hsu. Deep blue. Artificial intelligence, 134(1):57-83, 2002.

X. Du, J. Zhai, and K. Lv. Algorithm trading using q-learning and recurrent reinforcement learning. positions, 1:1.

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013. [OpenAIRE]

V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529-533, 2015.

V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. arXiv preprint arXiv:1602.01783, 2016. [OpenAIRE]

A. Nair, P. Srinivasan, S. Blackwell, C. Alcicek, R. Fearon, A. De Maria, V. Panneershelvam, M. Suleyman, C. Beattie, S. Petersen, et al. Massively parallel methods for deep reinforcement learning. arXiv preprint arXiv:1507.04296, 2015.

J. Schaeffer, J. Culberson, N. Treloar, B. Knight, P. Lu, and D. Szafron. A world championship caliber checkers program. Artificial Intelligence, 53(2):273-289, 1992.

S. Shalev-Shwartz, N. Ben-Zrihem, A. Cohen, and A. Shashua. Long-term planning by short-term prediction. arXiv preprint arXiv:1602.01580, 2016.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al. Mastering the game of go with deep neural networks and tree search. Nature, 529(7587):484-489, 2016.

G. Tesauro. Temporal difference learning and td-gammon. Communications of the ACM, 38(3): 58-68, 1995. [OpenAIRE]

J. Togelius, S. Karakovskiy, J. Koutn´ık, and J. Schmidhuber. Super mario evolution. In 2009 IEEE Symposium on Computational Intelligence and Games, pages 156-161. IEEE, 2009.

19 references, page 1 of 2
Abstract
Mastering a video game requires skill, tactics and strategy. While these attributes may be acquired naturally by human players, teaching them to a computer program is a far more challenging task. In recent years, extensive research was carried out in the field of reinforcement learning and numerous algorithms were introduced, aiming to learn how to perform human tasks such as playing video games. As a result, the Arcade Learning Environment (ALE) (Bellemare et al., 2013) has become a commonly used benchmark environment allowing algorithms to train on various Atari 2600 games. In many games the state-of-the-art algorithms outperform humans. In this paper we intro...
Subjects
ACM Computing Classification System: ComputingMilieux_PERSONALCOMPUTING
free text keywords: Computer Science - Learning, Computer Science - Artificial Intelligence
Related Organizations
Download from
19 references, page 1 of 2

Libretro. www.libretro.com. URL www.libretro.com. Accessed: 2016-11-03.

M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling. The arcade learning environment: An evaluation platform for general agents. Journal of Artificial Intelligence Research, 47:253-279, jun 2013. [OpenAIRE]

B. Bischoff, D. Nguyen-Tuong, I.-H. Lee, F. Streichert, and A. Knoll. Hierarchical reinforcement learning for robot navigation. In ESANN, 2013. [OpenAIRE]

G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba. Openai gym. arXiv preprint arXiv:1606.01540, 2016.

M. Campbell, A. J. Hoane, and F.-h. Hsu. Deep blue. Artificial intelligence, 134(1):57-83, 2002.

X. Du, J. Zhai, and K. Lv. Algorithm trading using q-learning and recurrent reinforcement learning. positions, 1:1.

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013. [OpenAIRE]

V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529-533, 2015.

V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu. Asynchronous methods for deep reinforcement learning. arXiv preprint arXiv:1602.01783, 2016. [OpenAIRE]

A. Nair, P. Srinivasan, S. Blackwell, C. Alcicek, R. Fearon, A. De Maria, V. Panneershelvam, M. Suleyman, C. Beattie, S. Petersen, et al. Massively parallel methods for deep reinforcement learning. arXiv preprint arXiv:1507.04296, 2015.

J. Schaeffer, J. Culberson, N. Treloar, B. Knight, P. Lu, and D. Szafron. A world championship caliber checkers program. Artificial Intelligence, 53(2):273-289, 1992.

S. Shalev-Shwartz, N. Ben-Zrihem, A. Cohen, and A. Shashua. Long-term planning by short-term prediction. arXiv preprint arXiv:1602.01580, 2016.

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al. Mastering the game of go with deep neural networks and tree search. Nature, 529(7587):484-489, 2016.

G. Tesauro. Temporal difference learning and td-gammon. Communications of the ACM, 38(3): 58-68, 1995. [OpenAIRE]

J. Togelius, S. Karakovskiy, J. Koutn´ık, and J. Schmidhuber. Super mario evolution. In 2009 IEEE Symposium on Computational Intelligence and Games, pages 156-161. IEEE, 2009.

19 references, page 1 of 2
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