Transfer learning in multi-agent systems through parallel transfer

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Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability (2017, background): “Taylor et al. (2013) introduce parallel transfer to accelerate multi-agent learning using inter-agent transfer.”
Multi-agent deep reinforcement learning: a survey (2021, background): “In general multi-agent systems, the works of (Boutsioukis et al. 2012; Taylor et al. 2013) substantiate that transfer learning can speed up the learning process.”
Learning in Multi-agent Systems with Sparse Interactions by Knowledge Transfer and Game Abstraction (2015, background): “A few approaches for knowledge transfer in multi-agent systems have been proposed, such as parallel transfer [20], equilibrium transfer [12], and training a classifier to generalize knowledge [23].”
Learning to Teach in Cooperative Multiagent Reinforcement Learning (2018, unknown): “These include imitation learning of expert demonstrations (Le et al. 2017), cooperative inverse reinforcement learning with a human and robot (Hadﬁeld-Menell et al. 2016), and transfer to parallel learners in tasks with similar value functions (Taylor et al. 2013).”