Abstract
Reinforcement learning (RL) is a learning approach based on behavioral psychology used by artificial agents to learn autonomously by interacting with their environment. An open issue in RL is the lack of visibility and understanding for end-users in terms of decisions taken by an agent during the learning process. One way to overcome this issue is to endow the agent with the ability to explain in simple terms why a particular action is taken in a particular situation. In this work, we propose a memory-based explainable reinforcement learning (MXRL) approach. Using an episodic memory, the RL agent is able to explain its decisions by using the probability of success and the number of transactions to reach the goal state. We have performed experiments considering two variations of a simulated scenario, namely, an unbounded grid world with aversive regions and a bounded grid world. The obtained results show that the agent, using information extracted from the memory, is able to explain its behavior in an understandable manner for non-expert end-users at any moment during its operation.
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- 1.
Agent, in this context, refers to any actor in an environment such as human, animal, or artificial agent.
References
Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018)
Conrad, B., Gross, D., Fogg, L., Ruchala, P.: Maternal confidence, knowledge, and quality of mother-toddler interactions: a preliminary study. Infant Mental Health J. 13(4), 353–362 (1992)
Cruz, F., Acuña, G., Cubillos, F., Moreno, V., Bassi, D.: Indirect training of grey-box models: application to a bioprocess. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4492, pp. 391–397. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72393-6_47
Cruz, F., Magg, S., Nagai, Y., Wermter, S.: Improving interactive reinforcement learning: what makes a good teacher? Connect. Sci. 30(3), 306–325 (2018)
Dulac-Arnold, G., Mankowitz, D., Hester, T.: Challenges of real-world reinforcement learning. arXiv preprint arXiv:1904.12901 (2019)
Gunning, D.: Explainable artificial intelligence (XAI). Defense Advanced Research Projects Agency (DARPA), nd Web (2017)
Hein, D., Udluft, S., Runkler, T.A.: Interpretable policies for reinforcement learning by genetic programming. Eng. Appl. Artif. Intell. 76, 158–169 (2018)
Langley, P., Meadows, B., Sridharan, M., Choi, D.: Explainable agency for intelligent autonomous systems. In: Twenty-Ninth IAAI Conference, pp. 4762–4763 (2017)
Lim, B.Y., Dey, A.K., Avrahami, D.: Why and why not explanations improve the intelligibility of context-aware intelligent systems. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2119–2128. ACM (2009)
Madumal, P., Miller, T., Sonenberg, L., Vetere, F.: Explainable reinforcement learning through a causal lens. arXiv preprint arXiv:1905.10958 (2019)
Miller, T.: Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1–38 (2018)
Niv, Y.: Reinforcement learning in the brain. J. Math. Psychol. 53, 139–154 (2009)
Pocius, R., Neal, L., Fern, A.: Strategic tasks for explainable reinforcement learning. In: The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019), p. 2 (2019)
Robertson, S.B., Weismer, S.E.: Effects of treatment on linguistic and social skills in toddlers with delayed language development. J. Speech Lang. Hearing Res. 42(5), 1234–1248 (1999)
Sequeira, P., Yeh, E., Gervasio, M.T.: Interestingness elements for explainable reinforcement learning through introspection. In: IUI Workshops, p. 7 (2019)
Shu, T., Xiong, C., Socher, R.: Hierarchical and interpretable skill acquisition in multi-task reinforcement learning. arXiv preprint arXiv:1712.07294 (2017)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. Bradford Book, Cambridge (1998)
Tabrez, A., Hayes, B.: Improving human-robot interaction through explainable reinforcement learning. In: 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), pp. 751–753. IEEE (2019)
Verma, A., Murali, V., Singh, R., Kohli, P., Chaudhuri, S.: Programmatically interpretable reinforcement learning. arXiv preprint arXiv:1804.02477 (2018)
Wang, X., Chen, Y., Yang, J., Wu, L., Wu, Z., Xie, X.: A reinforcement learning framework for explainable recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 587–596. IEEE (2018)
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Cruz, F., Dazeley, R., Vamplew, P. (2019). Memory-Based Explainable Reinforcement Learning. In: Liu, J., Bailey, J. (eds) AI 2019: Advances in Artificial Intelligence. AI 2019. Lecture Notes in Computer Science(), vol 11919. Springer, Cham. https://doi.org/10.1007/978-3-030-35288-2_6
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