Abstract
Recent breakthroughs in reinforcement learning have enabled the creation of learning agents for solving a wide variety of sequential decision problems. However, these methods require a large number of iterations in complex environments. A standard paradigm to tackle this challenge is to extend reinforcement learning to handle function approximation with deep learning. Lack of interpretability and impossibility to introduce background knowledge limits their usability in many safety-critical real-world scenarios. In this paper, we propose a new agent architecture to combine reinforcement learning and external knowledge. We derive a rule-based variant version of the Sarsa(\(\lambda \)) algorithm, which we call Sarsa-rb(\(\lambda \)), that augments data with complex knowledge and exploits similarities among states. We apply our method to a trading task from the stock market environment. We show that the resulting agent leads to much better performance but also improves training speed compared to the Deep Q-learning (DQN) algorithm and the Deep Deterministic Policy Gradients (DDPG) algorithm.
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References
Andre, D., Russell, S.J.: State abstraction for programmable reinforcement learning agents (2002)
Bellemare, M.G., Naddaf, Y., Veness, J., Bowling, M.: The arcade learning environment: an evaluation platform for general agents (2013)
Boots, B., Siddiqi, S.M., Gordon, G.J.: Closing the learning-planning loop with predictive state representations. Int. J. Robot. Res. 30(7), 954–966 (2011)
Bougie, N., Ichise, R.: Deep reinforcement learning boosted by external knowledge. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, pp. 331–338. ACM (2018)
d’Avila Garcez, A., Resende Riquetti Dutra, A., Alonso, E.: Towards Symbolic Reinforcement Learning with Common Sense. ArXiv e-prints, April 2018
Džeroski, S., De Raedt, L., Driessens, K.: Relational reinforcement learning. Mach. Learn. 43(1–2), 7–52 (2001)
Gambardella, L.M., Dorigo, M.: Ant-Q: a reinforcement learning approach to the traveling salesman problem. In: Machine Learning Proceedings 1995, pp. 252–260. Elsevier (1995)
Garnelo, M., Arulkumaran, K., Shanahan, M.: Towards deep symbolic reinforcement learning. In: Abbeel, P., Chen, P., Silver, D., Singh, S. (eds.) NIPS. Neural Information Processing Systems Foundation, La Jolla, California (2016)
Hausknecht, M., Stone, P.: Deep recurrent Q-learning for partially observable MDPs (2015)
Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971 (2015)
Mashayekhi, M., Gras, R.: Rule extraction from random forest: the RF+HC methods. In: Barbosa, D., Milios, E. (eds.) CANADIAN AI 2015. LNCS (LNAI), vol. 9091, pp. 223–237. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18356-5_20
Mnih, V., et al.: Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)
Nison, S.: Japanese Candlestick Charting Techniques: A Contemporary Guide to the Ancient Investment Techniques of the Far East. Penguin, London (2001)
Papudesi, V., Huber, M.: Learning behaviorally grounded state representations for reinforcement learning agents (2006)
Randløv, J., Alstrøm, P.: Learning to drive a bicycle using reinforcement learning and shaping. Proc. ICML 98, 463–471 (1998)
Rosencrantz, M., Gordon, G., Thrun, S.: Learning low dimensional predictive representations. In: Proceedings of the Twenty-First ICML. ACM (2004)
Singh, S.P., Jaakkola, T., Jordan, M.I.: Reinforcement learning with soft state aggregation. In: Proceedings of NIPS, pp. 361–368 (1995)
Singh, S.P., Sutton, R.S.: Reinforcement learning with replacing eligibility traces. Mach. Learn. 22(1–3), 123–158 (1996)
Sutton, R.S.: Generalization in reinforcement learning: successful examples using sparse coarse coding. In: Proceedings of NIPS, pp. 1038–1044 (1996)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Watkins, C.J., Dayan, P.: Q-learning. Mach. Learn. 8(3–4), 279–292 (1992)
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Bougie, N., Ichise, R. (2018). Abstracting Reinforcement Learning Agents with Prior Knowledge. In: Miller, T., Oren, N., Sakurai, Y., Noda, I., Savarimuthu, B.T.R., Cao Son, T. (eds) PRIMA 2018: Principles and Practice of Multi-Agent Systems. PRIMA 2018. Lecture Notes in Computer Science(), vol 11224. Springer, Cham. https://doi.org/10.1007/978-3-030-03098-8_27
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DOI: https://doi.org/10.1007/978-3-030-03098-8_27
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