Hostname: page-component-76c49bb84f-kjkt6 Total loading time: 0 Render date: 2025-07-06T21:58:02.480Z Has data issue: false hasContentIssue false
  • English
  • Français

Leveraging human knowledge in tabular reinforcement learning: a study of human subjects

Part of: Adaptive Learning Agents 2017

Published online by Cambridge University Press:  17 September 2018

Ariel Rosenfeld ,
Moshe Cohen ,
Matthew E. Taylor  and
Sarit Kraus
Ariel Rosenfeld
Affiliation:
Department of Management, Bar-Ilan University, Max and Anna Webb Street, 5290002 Ramat Gan, Israel; e-mail: arielros1@gmail.com
Moshe Cohen
Affiliation:
Department of Computer Science, Bar-Ilan University, Max and Anna Webb Street, 5290002 Ramat-Gan, Israel e-mail: moshec40@gmail.com; sarit@cs.biu.ac.il
Matthew E. Taylor
Affiliation:
Department of Computer Science, Washington State University, Pullman, WA 99164, USA e-mail: taylorm@eecs.wsu.edu
Sarit Kraus
Affiliation:
Department of Computer Science, Bar-Ilan University, Max and Anna Webb Street, 5290002 Ramat-Gan, Israel e-mail: moshec40@gmail.com; sarit@cs.biu.ac.il
Get access Rights & Permissions [Opens in a new window]

Abstract

Reinforcement learning (RL) can be extremely effective in solving complex, real-world problems. However, injecting human knowledge into an RL agent may require extensive effort and expertise on the human designer’s part. To date, human factors are generally not considered in the development and evaluation of possible RL approaches. In this article, we set out to investigate how different methods for injecting human knowledge are applied, in practice, by human designers of varying levels of knowledge and skill. We perform the first empirical evaluation of several methods, including a newly proposed method named State Action Similarity Solutions (SASS) which is based on the notion of similarities in the agent’s state–action space. Through this human study, consisting of 51 human participants, we shed new light on the human factors that play a key role in RL. We find that the classical reward shaping technique seems to be the most natural method for most designers, both expert and non-expert, to speed up RL. However, we further find that our proposed method SASS can be effectively and efficiently combined with reward shaping, and provides a beneficial alternative to using only a single-speedup method with minimal human designer effort overhead.

Information

Type
Adaptive and Learning Agents
Information
The Knowledge Engineering Review , Volume 33 , 2018 , e14
Copyright
© Cambridge University Press, 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Albus, J. S. 1981. Brains, Behavior and Robotics. McGraw-Hill Inc.Google Scholar
Benda, M. 1985. On Optimal Cooperation of Knowledge Sources. Technical report BCS-G2010-28.Google Scholar
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.Google Scholar
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).Google Scholar
Bruner, J. S. 1957. Going beyond the information given. Contemporary Approaches to Cognition 1(1), 119–160.Google Scholar
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.Google Scholar
Brys, T., Nowé, A., Kudenko, D. & Taylor, M. E. 2014. Combining multiple correlated reward and shaping signals by measuring confidence. In AAAI, 1687–1693.Google Scholar
Busoniu, L., Babuska, R., De Schutter, B. & Ernst, D. 2010. Reinforcement Learning and Dynamic Programming Using Function Approximators, 39 . CRC Press.Google Scholar
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.Google Scholar
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.Google Scholar
Girgin, S., Polat, F. & Alhajj, R. 2007. Positive impact of state similarity on reinforcement learning performance. IEEE Transactions on Cybernetics 37(5), 1256–1270.Google Scholar
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.Google Scholar
Hester, T. & Stone, P. 2013. Texplore: real-time sample-efficient reinforcement learning for robots. Machine Learning 90(3), 385–429.Google Scholar
Jong, N. K. & Stone, P. 2007. Model-based function approximation in reinforcement learning. In AAMAS, 95. ACM.Google Scholar
Karakovskiy, S. & Togelius, J. 2012. The Mario AI benchmark and competitions. IEEE Transactions on Computational Intelligence and AI in Games 4(1), 55–67.Google Scholar
Kelly, G. 1955. Personal Construct Psychology. Norton.Google Scholar
Knox, W. B. & Stone, P. 2010. Combining manual feedback with subsequent MDP reward signals for reinforcement learning. In Proceedings of AAMAS.Google Scholar
Leffler, B. R., Littman, M. L. & Edmunds, T. 2007. Efficient reinforcement learning with relocatable action models. AAAI 7, 572–577.Google Scholar
Littman, M. L. 1994. Markov games as a framework for multi-agent reinforcement learning. ICML 157, 157–163.Google Scholar
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.Google Scholar
Mataric, M. J. 1994. Reward functions for accelerated learning. In Machine Learning: Proceedings of the Eleventh International Conference, 181–189.Google Scholar
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.Google Scholar
Narayanamurthy, S. M. & Ravindran, B. 2008. On the hardness of finding symmetries in Markov decision processes. In ICML, 688–695.Google Scholar
Ng, A. Y., Harada, D. & Russell, S. 1999. Policy invariance under reward transformations: theory and application to reward shaping. ICML. 99, 278–287.Google Scholar
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.Google Scholar
Randløv, J. & Alstrøm, P. 1998. Learning to drive a bicycle using reinforcement learning and shaping. ICML 98, 463–471.Google Scholar
Ribeiro, C. H. 1995. Attentional mechanisms as a strategy for generalisation in the q-learning algorithm. Proceedings of ICANN 95, 455–460.Google Scholar
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.Google Scholar
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.Google Scholar
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.Google Scholar
Rosenfeld, A., Taylor, M. E. & Kraus, S. 2017b. Speeding up tabular reinforcement learning using stateaction similarities. In AAMAS, 1722–1724.Google Scholar
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.Google Scholar
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.Google Scholar
Skinner, B. F. 1958. Reinforcement today. American Psychologist 13(3), 94.Google Scholar
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.Google Scholar
Suay, H. B., Brys, T., Taylor, M. E. & Chernova, S. 2016. Learning from demonstration for shaping through inverse reinforcement learning. In AAMAS, 429–437.Google Scholar
Sutton, R. S. & Barto, A. G. 1998. Reinforcement Learning: An Introduction. MIT press.Google Scholar
Szepesvári, C. & Littman, M. L. 1999. ‘A unified analysis of value-function-based reinforcementlearning algorithms’. Neural Computation 11(8), 2017–2060.Google Scholar
Tamassia, M., Zambetta, F., Raffe, W., Mueller, F. & Li, X. 2016. Dynamic choice of state abstraction in q-learning. In ECAI.Google Scholar
Tanner, B. & White, A. 2009. RL-Glue : language-independent software for reinforcement-learning experiments. Journal of Machine Learning Research 10, 2133–2136.Google Scholar
Watkins, C. J. C. H. 1989. Learning from Delayed Rewards. PhD thesis, University of Cambridge.Google Scholar
Witten, I. H., Frank, E., Hall, M. A. & Pal, C. J. 2016. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.Google Scholar
Zinkevich, M. & Balch, T. 2001. Symmetry in Markov decision processes and its implications for single agent and multi agent learning. In ICML.Google Scholar