Abstract
Deep reinforcement learning algorithms such as Deep Q-Networks have successfully been used to construct a strong agent for Atari games by only performing direct evaluation of the current state and actions. This is in stark contrast to the algorithms for traditional board games such as Chess or Go, where a look-ahead search mechanism is indispensable to build a strong agent. In this paper, we present a novel deep reinforcement learning architecture that can both effectively and efficiently use information on future states in video games. First, we demonstrate that such information is indeed quite useful in deep reinforcement learning by using exact state transition information obtained from the emulator. We then propose a method that predicts future states using Long Short Term Memory (LSTM), such that the agent can look ahead without the emulator. In this work, we applied our method to the asynchronous advantage actor-critic (A3C) architecture. The experimental results show that our proposed method with predicted future states substantially outperforms the vanilla A3C in several Atari games.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
References
Bellemare, M.G., Naddaf, Y., Veness, J., Bowling, M.: The arcade learning environment: an evaluation platform for general agents. J. Artif. Intell. Res. 47, 253–279 (2013)
Bellemare, M., Srinivasan, S., Ostrovski, G., Schaul, T., Saxton, D., Munos, R.: Unifying count-based exploration and intrinsic motivation. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29, pp. 1471–1479. Curran Associates, Inc. (2016)
Bellemare, M., Veness, J., Bowling, M.: Investigating contingency awareness using Atari 2600 games. In: AAAI Conference on Artificial Intelligence, pp. 864–871 (2012)
Guo, X., Singh, S., Lewis, R., Lee, H.: Deep learning for reward design to improve Monte Carlo tree search in Atari games. In: Proceedings of 25th International Joint Conference on Artificial Intelligence, pp. 1519–1525 (2016)
Hausknecht, M., Stone, P.: Deep recurrent Q-learning for partially observable MDPs. In: 2015 AAAI Fall Symposium Series, pp. 29–37 (2015)
Jaderberg, M., Mnih, V., Czarnecki, W.M., Schaul, T., Leibo, J.Z., Silver, D., Kavukcuoglu, K.: Reinforcement learning with unsupervised auxiliary tasks. CoRR abs/1611.05397 (2016). http://arxiv.org/abs/1611.05397
Kocsis, L., Szepesvári, C.: Bandit based Monte-Carlo planning. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 282–293. Springer, Heidelberg (2006). https://doi.org/10.1007/11871842_29
Lin, L.J.: Reinforcement learning for robots using neural networks. Ph.D. thesis, Carnegie Mellon University (1992)
Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T.P., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. In: Proceedings of the 33rd International Conference on Machine Learning (ICML), pp. 1928–1937 (2016)
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M.: Playing Atari with deep reinforcement learning. In: NIPS Deep Learning Workshop (2013)
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)
Nair, A., Srinivasan, P., Blackwell, S., Alcicek, C., Fearon, R., De Maria, A., Panneershelvam, V., Suleyman, M., Beattie, C., Petersen, S., et al.: Massively parallel methods for deep reinforcement learning. In: ICML Deep Learning Workshop (2015)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-2010), pp. 807–814 (2010)
Osband, I., Blundell, C., Pritzel, A., Van Roy, B.: Deep exploration via bootstrapped DQN. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29, pp. 4026–4034. Curran Associates, Inc. (2016)
Schulman, J., Levine, S., Abbeel, P., Jordan, M., Moritz, P.: Trust region policy optimization. In: Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), pp. 1889–1897 (2015)
Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., Hassabis, D.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)
Sutton, R.S.: Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In: Proceedings of the 7th International Conference on Machine Learning, pp. 216–224 (1990)
Sutton, R.S.: Dyna, an integrated architecture for learning, planning, and reacting. SIGART Bull. 2(4), 160–163 (1991)
Tang, H., Houthooft, R., Foote, D., Stooke, A., Chen, X., Duan, Y., Schulman, J., De Turck, F., Abbeel, P.: #Exploration: a study of count-based exploration for deep reinforcement learning. In: NIPS Deep Reinforcement Learning Workshop (2016)
Tieleman, T., Hinton, G.: Lecture 6e RMSprop: divide the gradient by a running average of its recent magnitude. Coursera: Neural Networks for Machine Learning (2012)
Tokui, S., Oono, K., Hido, S., Clayton, J.: Chainer: a next-generation open source framework for deep learning. In: Proceedings of Workshop on Machine Learning Systems (LearningSys) in The Twenty-Ninth Annual Conference on Neural Information Processing Systems (NIPS) (2015)
Van Hasselt, H., Guez, A., Silver, D.: Deep reinforcement learning with double Q-learning. In: AAAI Conference on Artificial Intelligence, pp. 2094–2100 (2016)
Wang, Z., de Freitas, N., Lanctot, M.: Dueling network architectures for deep reinforcement learning. CoRR abs/1511.06581 (2015). http://arxiv.org/abs/1511.06581
Watkins, C.J.C.H.: Learning from delayed rewards. Ph.D. thesis, University of Cambridge, England (1989)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Kameko, H., Suzuki, J., Mizukami, N., Tsuruoka, Y. (2018). Deep Reinforcement Learning with Hidden Layers on Future States. In: Cazenave, T., Winands, M., Saffidine, A. (eds) Computer Games. CGW 2017. Communications in Computer and Information Science, vol 818. Springer, Cham. https://doi.org/10.1007/978-3-319-75931-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-75931-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-75930-2
Online ISBN: 978-3-319-75931-9
eBook Packages: Computer ScienceComputer Science (R0)