Albus J. S. 1981. Brains, Behavior and Robotics. McGraw-Hill Inc.

Benda M. 1985. On Optimal Cooperation of Knowledge Sources. Technical report BCS-G2010-28.

Bianchi R. A., Martins M. F., Ribeiro C. H. & Costa A. H. 2014. ‘Heuristically-accelerated multiagent reinforcement learning’. IEEE Transactions on Cybernetics 44(2), 252–265.

Brockman G., Cheung V., Pettersson L., Schneider J., Schulman J., Tang J. & Zaremba W. 2016. Openai gym. https://gym.openai.com (accessed 24 October 2017).

Bruner J. S. 1957. Going beyond the information given. Contemporary Approaches to Cognition 1(1), 119–160.

Brys T., Harutyunyan A., Suay H. B., Chernova S., Taylor M. E. & Nowé A. 2015. Reinforcement learning from demonstration through shaping. In IJCAI, 3352–3358.

Brys T., Nowé A., Kudenko D. & Taylor M. E. 2014. Combining multiple correlated reward and shaping signals by measuring confidence. In AAAI, 1687–1693.

Busoniu L., Babuska R., De Schutter B. & Ernst D. 2010. Reinforcement Learning and Dynamic Programming Using Function Approximators, 39. CRC Press.

Devlin S., Grze´s M. & Kudenko D. 2011. Multi-agent, reward shaping for robocup keepaway. In The 10th International Conference on Autonomous Agents and Multiagent Systems-Volume 3, 1227–1228. International Foundation for Autonomous Agents and Multiagent Systems.

Geramifard A., Klein R. H., Dann C., Dabney W. & How J. P. 2013. RLPy: The Reinforcement Learning Library for Education and Research. http://acl.mit.edu/RLPy.

Girgin S., Polat F. & Alhajj R. 2007. Positive impact of state similarity on reinforcement learning performance. IEEE Transactions on Cybernetics 37(5), 1256–1270.

Hart S. G. & Staveland L. E. 1988. Development of NASA-TLX (task load index): results of empirical and theoretical research. Advances in Psychology 52, 139–183.

Hester T. & Stone P. 2013. Texplore: real-time sample-efficient reinforcement learning for robots. Machine Learning 90(3), 385–429.

Jong N. K. & Stone P. 2007. Model-based function approximation in reinforcement learning. In AAMAS, 95. ACM.

Karakovskiy S. & Togelius J. 2012. The Mario AI benchmark and competitions. IEEE Transactions on Computational Intelligence and AI in Games 4(1), 55–67.

Kelly G. 1955. Personal Construct Psychology. Norton.

Knox W. B. & Stone P. 2010. Combining manual feedback with subsequent MDP reward signals for reinforcement learning. In Proceedings of AAMAS.

Leffler B. R., Littman M. L. & Edmunds T. 2007. Efficient reinforcement learning with relocatable action models. AAAI 7, 572–577.

Littman M. L. 1994. Markov games as a framework for multi-agent reinforcement learning. ICML 157, 157–163.

Martins M. F. & Bianchi R. A. 2013. Heuristically-accelerated reinforcement learning: a comparative analysis of performance. In Conference Towards Autonomous Robotic Systems, 15–27. Springer.

Mataric M. J. 1994. Reward functions for accelerated learning. In Machine Learning: Proceedings of the Eleventh International Conference, 181–189.

Mnih V., Kavukcuoglu K., Silver D., Rusu A. A., Veness J., Bellemare M. G., Graves A., Riedmiller M., Fidjeland A. K., Ostrovski G., Petersen S., Beattie C., Sadik A., Antonoglou I., King H., Kumaran D., Wierstra D., Legg S. & Hassabis D. 2015. Human-level control through deep reinforcement learning. Nature 518(7540), 529–533.

Narayanamurthy S. M. & Ravindran B. 2008. On the hardness of finding symmetries in Markov decision processes. In ICML, 688–695.

Ng A. Y., Harada D. & Russell S. 1999. Policy invariance under reward transformations: theory and application to reward shaping. ICML. 99, 278–287.

Peng B., MacGlashan J., Loftin R., Littman M. L., Roberts D. L. & Taylor M. E. 2016. A need for speed: adapting agent action speed to improve task learning from non-expert humans. In AAMAS, 957–965.

Randløv J. & Alstrøm P. 1998. Learning to drive a bicycle using reinforcement learning and shaping. ICML 98, 463–471.

Ribeiro C. H. 1995. Attentional mechanisms as a strategy for generalisation in the q-learning algorithm. Proceedings of ICANN 95, 455–460.

Ribeiro C. & Szepesv´ari C. 1996. Q-learning combined with spreading: convergence and results. In Proceedings of the ISRF-IEE International Conference on Intelligent and Cognitive Systems (Neural Networks Symposium), 32–36.

Rosenfeld A. & Kraus S. 2018. Predicting human decision-making: from prediction to action. Synthesis Lectures on Artificial Intelligence and Machine Learning 12(1), 1–150.

Rosenfeld A., Taylor M. E. & Kraus S. 2017a. Leveraging human knowledge in tabular reinforcement learning: a study of human subjects. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, August 19–25, 2017, 3823–3830.

Rosenfeld A., Taylor M. E. & Kraus S. 2017b. Speeding up tabular reinforcement learning using stateaction similarities. In AAMAS, 1722–1724.

Schaul T., Bayer J., Wierstra D., Sun Y., Felder M., Sehnke F., Rückstieß T & Schmidhuber J. 2010. PyBrain, Journal of Machine Learning Research 11, 743–746.

Sequeira P., Melo F. S. & Paiva A. 2013. An associative state-space metric for learning in factored mdps. In Portuguese Conference on Artificial Intelligence, 163–174. Springer.

Skinner B. F. 1958. Reinforcement today. American Psychologist 13(3), 94.

Stone P., Kuhlmann G., Taylor M. E. & Liu Y. 2006. Keepaway soccer: from machine learning testbed to benchmark. In RoboCup-2005: Robot Soccer World Cup IX, I. Noda, A. Jacoff, A. Bredenfeld & Y. Takahashi (eds). Springer Verlag 4020, 93–105.

Suay H. B., Brys T., Taylor M. E. & Chernova S. 2016. Learning from demonstration for shaping through inverse reinforcement learning. In AAMAS, 429–437.

Sutton R. S. & Barto A. G. 1998. Reinforcement Learning: An Introduction. MIT press.

Szepesvári C. & Littman M. L. 1999. ‘A unified analysis of value-function-based reinforcementlearning algorithms’. Neural Computation 11(8), 2017–2060.

Tamassia M., Zambetta F., Raffe W., Mueller F. & Li X. 2016. Dynamic choice of state abstraction in q-learning. In ECAI.

Tanner B. & White A. 2009. RL-Glue : language-independent software for reinforcement-learning experiments. Journal of Machine Learning Research 10, 2133–2136.

Watkins C. J. C. H. 1989. Learning from Delayed Rewards. PhD thesis, University of Cambridge.

Witten I. H., Frank E., Hall M. A. & Pal C. J. 2016. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.

Zinkevich M. & Balch T. 2001. Symmetry in Markov decision processes and its implications for single agent and multi agent learning. In ICML.