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Articles

Reinforcement learning agents providing advice in complex video games

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Pages 45-63 | Received 01 Sep 2013, Accepted 19 Nov 2013, Published online: 13 Mar 2014

References

  • Abbeel, P., & Ng, A. Y. (2004). Apprenticeship learning via inverse reinforcement learning. Proceedings of the international conference on machine learning, Banff.
  • Albus, J. S. (1981). Brains, behavior, and robotics. Peterborough, NH: Byte Books.
  • Argall, B., Chernova, S., Veloso, M., & Browning, B. (2009). A survey of robot learning from demonstration. Robotics and Autonomous Systems, 57(5), 469–483. doi: 10.1016/j.robot.2008.10.024
  • Babes-Vroman, M., Mari, V., Subramanian, K., & Littman, M. (2010). Apprenticeship learning about multiple intentions. Proceeding of the international conference on machine learning, Haifa.
  • Cakmak, M., & Lopes, M. (2012). Algorithmic and human teaching of sequential decision tasks. AAAI conference on artificial intelligence, Toronto.
  • Carboni, N., & Taylor, M. E. (2013, May). Preliminary results for 1 vs. 1 tactics in StarCraft. Proceedings of the adaptive and learning agents workshop (at AAMAS-13), Saint Paul, MN.
  • Chakraborty, D., & Sen, S. (2006). Teaching new teammates. Proceedings of the conference on autonomous agents and multiagent systems, Hakodate.
  • Clouse, J. A. (1996). On integrating apprentice learning and reinforcement learning (PhD thesis). University of Massachusetts, Amherst, MA.
  • Joachims, T. (1999). Making large-scale SVM learning practical. In B. Scholkopf, C. Burges, and A. Smola (Eds.), Advances in kernel methods – support vector learning (pp. 41–56). Cambridge, MA: MIT Press.
  • Khan, F., Zhu, X. J., & Mutlu, B. (2011) How do humans teach: On curriculum learning and teaching dimension. Proceedings of advances in Neural Information Processing Systems, Granada.
  • Knox, W. B., Glass, B. D., Love, B. C., Maddox, W. T., & Stone, P. (2012). How humans teach agents – A new experimental perspective. International Journal of Social Robotics, 4(4), 409–421. doi: 10.1007/s12369-012-0163-x
  • Lin, L. J. (1992). Self-improving reactive agents based on reinforcement learning, planning and teaching. Machine Learning, 8, 293–321.
  • Neu, G. (2007). Apprenticeship learning using inverse reinforcement learning and gradient methods. Proceedings of the conference on uncertainty in artificial intelligence, Vancouver.
  • Nunes, L., & Oliveira, E. (2003). On learning by exchanging advice. The Interdisciplinary Journal of Artificial Intelligence & the Simulation of Behaviour, 1(3), 241–256.
  • Price, B., & Boutilier, C. (2003). Accelerating reinforcement learning through implicit imitation. Journal of Artificial Intelligence Research, 19, 569–629.
  • Rohlfshagen, P., & Lucas, S. M. (2011). Ms Pac-Man versus Ghost Team CEC 2011 competition. Proceedings of the congress on evolutionary computation, New Orleans, LA.
  • Stone, P., Kaminka, G. A., Kraus, S., & Rosenschein, J. S. (2010). Ad hoc autonomous agent teams: Collaboration without pre-coordination. In M. Fox and D. Poole (Eds.), Proceedings of the AAAI Conference on Artificial Intelligence, Atlanta, GA (pp. 1504–1509).
  • Sutton, R. S., & Barto, A. G. (1998). Introduction to reinforcement learning. Cambridge, MA: MIT Press.
  • Taylor, M. E., & Stone, P. (2007). Cross-domain transfer for reinforcement learning. Proceedings of the international conference on machine learning, Corvalis, OR.
  • Taylor, M. E., & Stone, P. (2009). Transfer learning for reinforcement learning domains: A survey. Journal of Machine Learning Research, 10(1), 1633–1685.
  • Taylor, M. E., Suay, H. B., & Chernova, S. (2011). Integrating reinforcement learning with human demonstrations of varying ability. Proceedings of the international conference on autonomous agents and multiagent systems, Taipei.
  • Torrey, L., & Taylor, M. E. (2013). Teaching on a budget: Agents advising agents in reinforcement learning. Proceedings of the international conference on autonomous agents and multiagent systems, Saint Paul, MN.
  • Walsh, T. J., & Goschin, S. (2012). Dynamic teaching in sequential decision making environments. Conference on uncertainty in artificial intelligence, Catalina Island, CA.
  • Ziebart, B., Maas, A., Bagnell, J. A. D., & Dey, A. (2008). Maximum entropy inverse reinforcement learning. Proceeding of AAAI conference on artificial intelligence, Chicago, IL.

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