Teacher-Student Framework: a Reinforcement Learning Approach

By Matthieu Zimmer, P. Viappiani, Paul Weng. 2014

68 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Continual Learning in the Teacher-Student Setup: Impact of Task Similarity (2021, background): “…1993; Engel & Van den Broeck, 2001; Zdeborov ´ a & Krzakala, 2016) and has recently experienced a surge of activity in the machine learning community (Zimmer et al., 2014; Zhong et al., 2017; Tian, 2017; Du et al., 2018; Soltanolkotabi et al., 2018; Aubin et al., 2018; Saxe et al., 2018;…”
Learning to Teach in Cooperative Multiagent Reinforcement Learning (2018, background): “ng progress as reward feedback, resulting in teachers that improve the rate of learning without harming ﬁnal performance. In contrast to related works (Torrey & Taylor, 2013; Taylor et al., 2014; Zimmer et al., 2014), our work addresses the novel setting of peer-to-peer teaching, even when peers”
Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning (2020, background): “Training agents from trajectories [30] of other agents [40] or humans [34] is a common case of teaching agents.”
Online Transfer Learning in Reinforcement Learning Domains (2015, methodology): “Although this advice method is shown to empirically provides multiple beneﬁts Tor-rey and Taylor (2013); Zimmer et al. (2014), existing work does not provide a formal understanding of teaching or advice.”
A Q-values Sharing Framework for Multi-agent Reinforcement Learning under Budget Constraint (2020, background): “This setting is also widely accepted by many extensions of the teacher-student framework, as depicted in [27, 28, 5].”