Learning teaching strategies in an Adaptive and Intelligent Educational System through Reinforcement Learning

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A Multi-Agent Framework for Packet Routing in Wireless Sensor Networks (2015, background): “problems, such as cliff-walking [30], to more complex ones, such as web-based education [31] and face recognition [32].”
Tackling Climate Change with Machine Learning (2019, methodology): “[682] Ana Iglesias, Paloma Martı́nez, Ricardo Aler, and Fernando Fernández.”
Dynamic analysis of multiagent Q-learning with ε-greedy exploration (2009, methodology): “Q-learning is certainly one of the most studied Reinforcement Learning (RL) algorithm and has been applied with success in several domains, from relatively simple toy problems, such as Cliff-Walking (Sutton & Barto, 1998), to more complex ones, such as web-based education (Iglesias et al., 2008) and”
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey (2020, methodology): “Curriculum learning has also been widely used for building effective Intelligent Tutoring Systems (ITS) for human education (Iglesias et al., 2003, 2009; Green et al., 2011; Brunskill and Russell, 2011; Doroudi et al., 2016).”