Abstract
Agents that acquire their own policies autonomously have the risk of accidents caused by the agents’ unexpected behavior. Therefore, it is necessary to improve the predictability of the agents’ behavior in order to ensure the safety. Instruction-based Behavior Explanation (IBE) is a method for a reinforcement learning agent to announce the agent’s future behavior. However, it was not verified that the IBE was applicable to an agent that changes the policy dynamically. In this paper, we consider agents under training and improve the IBE for the application to agents with changing policy. We conducted an experiment to verify if the behavior explanation model of an immature agent worked even after the agent’s further training. The results indicated the applicability of the improved IBE to agents under training.
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References
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., Man, D.: Concrete problems in AI safety. arXiv preprint (2016). arXiv:1606.06565
Le, Q.V.: Building high-level features using large scale unsupervised learning. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 8595–8598 (2013)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). doi:10.1007/978-3-319-10590-1_53
Simonyan, K., Vedaldi, A., Zisserman, A.: Deep inside convolutional networks: visualising image classification models and saliency maps. arXiv preprint (2013). arXiv:1312.6034
Elizalde, F., Sucar, L.E., Luque, M., Dez, F.J., Ballesteros, A.R.: Policy explanation in factored markov decision processes. In: Proceedings of the 4th European Workshop on Probabilistic Graphical Models (2008)
Hayes, B., Shah, J.A.: Improving robot controller transparency through autonomous policy explanation. In: Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction, pp. 303–312. ACM (2017)
Fukuchi, Y., Osawa, M., Yamakawa, H., Imai, M.: Autonomous self-explanation of behavior for interactive reinforcement learning agents. In: Proceedings of the 5th International Conference on Human Agent Interaction (2017)
Knox, W.B., Stone, P.: Interactively shaping agents via human reinforcement: the tamer framework. In: Proceedings of the Fifth International Conference on Knowledge Capture, pp. 9–16. ACM (2009)
Cruz, F., Magg, S., Weber, C., Wermter, S.: Training agents with interactive reinforcement learning and contextual affordances. IEEE Trans. Cognit. Dev. Syst. 8(4), 271–284 (2016)
Thomaz, A.L., Breazeal, C.: Reinforcement learning with human teachers: evidence of feedback and guidance with implications for learning performance. In: The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference (AAAI), vol. 6, pp. 1000–1005 (2006)
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., Zaremba, W.: Openai gym. arXiv preprint (2016). arXiv:1606.01540
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, G.B., 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.: Human level control through deep reinforcement learning. Nature 518, 529–533 (2017)
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Fukuchi, Y., Osawa, M., Yamakawa, H., Imai, M. (2017). Application of Instruction-Based Behavior Explanation to a Reinforcement Learning Agent with Changing Policy. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_11
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DOI: https://doi.org/10.1007/978-3-319-70087-8_11
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