Improving the Performance of Backward Chained Behavior Trees that use Reinforcement Learning

R. A. Howard, Dynamic Programming and Markov Processes . Cambridge, MA: MIT Press, 1960

1960

Earlier work this paper cites.

M. Mateas and A. Stern, “Façade: An experiment in building a fully-realized interactive drama,” in Game developers conference , vol. 2, 2003, pp. 4–8

2003

Earlier work this paper cites.

C.-U. Lim, R. Baumgarten, and S. Colton, “Evolving behaviour trees for the commercial game defcon,” in European conference on the applications of evolutionary computation . Springer, 2010, pp. 100–110

2010

Earlier work this paper cites.

R. Dey and C. Child, “Ql-bt: Enhancing behaviour tree design and implementation with q-learning,” in 2013 IEEE Conference on Computational Inteligence in Games (CIG) . IEEE, 2013, pp. 1–8

2013

Earlier work this paper cites.

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” 2013

2013

Earlier work this paper cites.

R. d. P. Pereira and P. M. Engel, “A framework for constrained and adaptive behavior-based agents,” arXiv preprint arXiv:1506.02312 , 2015

Original

2015

Earlier work this paper cites.

Y. Fu, L. Qin, and Q. Yin, “A reinforcement learning behavior tree framework for game ai,” in Proceedings of the 2016 International Conference on Economics, Social Science, Arts, Education and Management Engineering , 2016

2016

Earlier work this paper cites.

B. Hannaford, D. Hu, D. Zhang, and Y. Li, “Simulation results on selector adaptation in behavior trees,” arXiv preprint arXiv:1606.09219 , 2016

Original

2016

Earlier work this paper cites.

M. Nicolau, D. Perez-Liebana, M. O’Neill, and A. Brabazon, “Evolutionary behavior tree approaches for navigating platform games,” IEEE Transactions on Computational Intelligence and AI in Games , vol. 9, no. 3, pp. 227–238, 2016

2016

Earlier work this paper cites.

M. Johnson, K. Hofmann, T. Hutton, and D. Bignell, “The malmo platform for artificial intelligence experimentation,” in Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence , ser. IJCAI’16. AAAI Press, 2016, p. 4246–4247

2016

Cited alongside, same era.

G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, “Openai gym,” arXiv preprint arXiv:1606.01540 , 2016

Original

2016

Cited alongside, same era.

V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” 2016

2016

Cited alongside, same era.

Q. Zhang, L. Sun, P. Jiao, and Q. Yin, “Combining behavior trees with maxq learning to facilitate cgfs behavior modeling,” in 2017 4th International Conference on Systems and Informatics (ICSAI) . IEEE, 2017, pp. 525–531

2017

Cited alongside, same era.

O. Vinyals, I. Babuschkin, W. M. Czarnecki, M. Mathieu, A. Dudzik, J. Chung, D. H. Choi, R. Powell, T. Ewalds, P. Georgiev, et al. , “Grandmaster level in starcraft ii using multi-agent reinforcement learning,” Nature , vol. 575, no. 7782, pp. 350–354, 2019

2019

Later among the works it cites.

C. Paduraru and M. Paduraru, “Automatic difficulty management and testing in games using a framework based on behavior trees and genetic algorithms,” in 2019 24th International Conference on Engineering of Complex Computer Systems (ICECCS) . IEEE, 2019, pp. 170–179

2019

Later among the works it cites.

M. Colledanchise, D. Almeida, and P. Ögren, “Towards blended reactive planning and acting using behavior trees,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 8839–8845

2019

Later among the works it cites.

A. Raffin, A. Hill, M. Ernestus, A. Gleave, A. Kanervisto, and N. Dormann, “Stable baselines3,” \url

2019

Later among the works it cites.