Abstract
In this paper we investigate reinforcement learning approaches for the popular computer game Tetris. User-defined reward functions have been applied to TD(0) learning based on ε-greedy strategies in the standard Tetris scenario. The numerical experiments show that reinforcement learning can significantly outperform agents utilizing fixed policies.
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References
Bellman, R.E.: Dynamic Programming. Dover Publications (2003)
Burgiel, H.: How to lose at Tetris. Mathematical Gazette 81, 194–200 (1997)
Demaine, E.D., Hohenberger, S., Liben-Nowell, D.: Tetris is hard, even to approximate. In: Warnow, T.J., Zhu, B. (eds.) COCOON 2003. LNCS, vol. 2697, Springer, Heidelberg (2003)
Faußer, S., Schwenker, F.: Neural approximation of Monte Carlo policy evaluation deployed in Connect Four. In: Prevost, L., Marinai, S., Schwenker, F. (eds.) ANNPR 2008. LNCS (LNAI), vol. 5064, pp. 90–100. Springer, Heidelberg (2008)
Faußer, S., Schwenker, F.: Learning a strategy with neural approximated temporal-difference methods in English Draughts. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 2925–2928. IEEE (2010)
Faußer, S., Schwenker, F.: Ensemble methods for reinforcement learning with function approximation. In: Sansone, C., Kittler, J., Roli, F. (eds.) MCS 2011. LNCS, vol. 6713, pp. 56–65. Springer, Heidelberg (2011)
Faußer, S., Schwenker, F.: Neural network ensembles in reinforcement learning. Neural Processing Letters, 1–15 (2013)
Groß, A., Friedland, J., Schwenker, F.: Learning to play Tetris applying reinforcement learning methods. In: ESANN, pp. 131–136 (2008)
Sutton, R.S., Barto, A.G.: Introduction to Reinforcement Learning. MIT Press, Cambridge (1998)
Tesauro, G.: Temporal difference learning and TD-Gammon. Commun. ACM 38(3), 58–68 (1995)
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Thiam, P., Kessler, V., Schwenker, F. (2014). A Reinforcement Learning Algorithm to Train a Tetris Playing Agent. In: El Gayar, N., Schwenker, F., Suen, C. (eds) Artificial Neural Networks in Pattern Recognition. ANNPR 2014. Lecture Notes in Computer Science(), vol 8774. Springer, Cham. https://doi.org/10.1007/978-3-319-11656-3_15
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DOI: https://doi.org/10.1007/978-3-319-11656-3_15
Publisher Name: Springer, Cham
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