S. Afantenos, E. Kow, N. Asher, and J. Perret, Discourse parsing for multi-party chat dialogues, Proc. EMNLP 2015, pp.928-937, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01535954

O. Anschel, N. Baram, and N. Shimkin, Deep reinforcement learning with averaged target DQN, 2016.

R. Bellman, Dynamic programming, Courier Corporation, 2013.

H. Cuayáhuitl, S. Keizer, and O. Lemon, Strategic dialogue management via deep reinforcement learning, Proceedings of the NIPS Deep Reinforcement Learning Workshop (NIPS 2015, 2015.

R. Dearden, N. Friedman, and S. Russell, Bayesian Q-learning, AAAI/IAAI, pp.761-768, 1998.

M. S. Dobre and A. Lascarides, Online learning and mining human play in complex games, 2015 IEEE Conference on Computational Intelligence and Games (CIG), pp.60-67, 2015.

P. Finnman and M. Winberg, Deep reinforcement learning compared with Q-table learning applied to backgammon, 2016.

M. Guhe and A. Lascarides, Game strategies for the Settlers of Catan, Computational Intelligence and Games (CIG), pp.1-8, 2014.

H. Van-hasselt, A. Guez, and D. Silver, Deep reinforcement learning with double Q-learning, 2015.

M. Hausknecht and P. Stone, Deep recurrent Q-learning for partially observable MDPs, vol.7, 2015.

S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Comput, vol.9, issue.8, pp.1735-1780, 1997.

E. Karamalegkos, Monte Carlo tree search in the "Settlers of Catan" strategy game, Senior Undergraduate Diploma thesis, 2014.

S. Keizer, Evaluating persuasion strategies and deep reinforcement learning methods for negotiation dialogue agents, Proceedings of the 15th Conference of the European Chapter, vol.2, pp.480-484, 2017.

J. R. Kok and N. Vlassis, Collaborative multiagent reinforcement learning by payoff propagation, J. Mach. Learn. Res, vol.7, pp.1789-1828, 2006.

M. Lai, Giraffe: using deep reinforcement learning to play Chess, 2015.

T. P. Lillicrap, Continuous control with deep reinforcement learning, 2015.

V. Mnih, K. Kavukcuoglu, and D. Silver, Human-level control through deep reinforcement learning, Nature, vol.518, issue.7540, pp.529-533, 2015.

J. Oh, X. Guo, H. Lee, R. L. Lewis, and S. Singh, Action-conditional video prediction using deep networks in Atari games, Advances in Neural Information Processing Systems, pp.2863-2871, 2015.

I. Osband, C. Blundell, A. Pritzel, and B. V. Roy, Deep exploration via bootstrapped DQN, 2016.

K. P. Panousis, Real-time planning and learning in the "Settlers of Catan" strategy game, Senior Undergraduate Diploma thesis, 2014.

M. Pfeiffer, Reinforcement learning of strategies for Settlers of Catan, International Conference on Computer Games: Artificial Intelligence, 2018.

S. J. Russell and A. Zimdars, Q-decomposition for reinforcement learning agents, Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp.656-663, 2003.

J. Schmidhuber, Deep learning in neural networks: an overview, Neural Netw, vol.61, pp.85-117, 2015.

D. Silver, Mastering the game of go without human knowledge, Nature, vol.550, pp.354-359, 2017.

P. Stone and M. Veloso, Team-partitioned, opaque-transition reinforcement learning, Proceedings of the Third Annual Conference on Autonomous Agents, pp.206-212, 1999.

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 1998.

I. Szita, G. Chaslot, and P. Spronck, Monte-Carlo tree search in Settlers of Catan, ACG 2009, vol.6048, pp.21-32

, , 2010.

R. S. Thomas, Real-time decision making for adversarial environments using a plan-based heuristic, 2003.