RL Techniques for the Teacher–Student Framework
The teacher–student framework has been introduced as a way to improve sample efficiency by deploying an advising mechanism. This mechanism involves a teacher guiding the student’s exploration. Previous studies in this field have focused on the teacher advising the student on the optimal action to take in a given state. However, Anand et al. [1] proposed extending this advising mechanism by incorporating qualitative assessments of the state provided by the teacher, leveraging their domain expertise to provide more informative signals. To effectively reuse the advice provided by the teacher, a novel architecture called Advice Replay Memory (ARM ) has been introduced. Zimmer et al. [2] presented a novel RL approach to teaching another RL agent, referred to as the student, in the context of “teaching on a budget”, where the teacher agent can only provide a limited number of suggestions. Li et al. [3] proposed an adaptive learning system using a continuous latent trait model and a deep Q-learning algorithm. The system aims to improve learners’ abilities to reach target levels. See also [4]
Example:
An example for a teacher–student framework using RL is a practical and effective approach for designing distance education resources for children aged from three to five years, see [4].
Reference:
[1] Anand, D.; Gupta, V.; Paruchuri, P.; Ravindran, B. An enhanced advising model in teacher-student framework using state categorization. In Proceedings of the AAAI Conference on Artificial Intelligence, Virtually, 2–9 February 2021; Volume 35, pp. 6653–6660.
[2] Zimmer, M.; Viappiani, P.; Weng, P. Teacher-student framework: A reinforcement learning approach. In Proceedings of the AAMAS Workshop Autonomous Robots and Multirobot Systems, Paris, France, 5–9 May 2014.
[3] Li, X.; Xu, H.; Zhang, J.; Chang, H.h. Deep reinforcement learning for adaptive learning systems. J. Educ. Behav. Stat. 2023, 48, 220–243.
[4] Fahad Mon, B.; Wasfi, A.; Hayajneh, M.; Slim, A.; Abu Ali, N. Reinforcement Learning in Education: A Literature Review. Informatics 2023, 10, 74. https://doi.org/10.3390/informatics10030074
[2] Zimmer, M.; Viappiani, P.; Weng, P. Teacher-student framework: A reinforcement learning approach. In Proceedings of the AAMAS Workshop Autonomous Robots and Multirobot Systems, Paris, France, 5–9 May 2014.
[3] Li, X.; Xu, H.; Zhang, J.; Chang, H.h. Deep reinforcement learning for adaptive learning systems. J. Educ. Behav. Stat. 2023, 48, 220–243.
[4] Fahad Mon, B.; Wasfi, A.; Hayajneh, M.; Slim, A.; Abu Ali, N. Reinforcement Learning in Education: A Literature Review. Informatics 2023, 10, 74. https://doi.org/10.3390/informatics10030074
Author of the tip:
Ivo Nowak
HAW Hamburg