Using Monte Carlo Search with Data Aggregation to Improve Robot Soccer Policies

Author:

Riccio Francesco,Capobianco Roberto,Nardi Daniele

Publisher

Springer International Publishing

Reference15 articles.

1. Biswas, J., Mendoza, J.P., Zhu, D., Choi, B., Klee, S., Veloso, M.: Opponent-driven planning and execution for pass, attack, and defense in a multi-robot soccer team. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, pp. 493–500. International Foundation for Autonomous Agents and Multiagent Systems (2014)

2. Chang, K.W., Krishnamurthy, A., Agarwal, A., Daume, H., Langford, J.: Learning to search better than your teacher. In: Proceedings of the 32nd International Conference on Machine Learning (ICML 2015), pp. 2058–2066 (2015)

3. Han, K., Veloso, M.: Automated robot behavior recognition applied to robotic soccer. In: Hollerbach, J.M., Koditschek, D.E. (eds.) Robotics Research, pp. 249–256. Springer, London (2000). https://doi.org/10.1007/978-1-4471-0765-1_30. Also in the Proceedings of IJCAI-99 Workshop on Team Behaviors and Plan Recognition

4. Ijspeert, A.J., Nakanishi, J., Schaal, S.: Trajectory formation for imitation with nonlinear dynamical systems. In: Proceedings of the 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2, pp. 752–757. IEEE (2001)

5. Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., Osawa, E.: RoboCup: the robot world cup initiative. In: Proceedings of the First International Conference on Autonomous Agents, pp. 340–347. ACM (1997)

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