Multiagent Reinforcement Learning for Multi-Robot Systems: A Survey

By Erfu Yang, Dongbing Gu. 2004

195 citations recorded by the source corpus.

Subfields: Reinforcement Learning, Robotics & Control

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Citations in the corpus

Counterfactual Multi-Agent Policy Gradients (2017, background): “Although multi-agent RL has been applied in a variety of settings (Busoniu et al., 2008; Yang & Gu, 2004), it has often been restricted to tabular methods and simple environments.”
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning (2017, background): “Multi-agent RL has a rich history (Busoniu et al., 2008; Yang & Gu, 2004) but has mostly focused on tabular settings and simple environments.”
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning (2018, background): “Recent work in multi-agent RL has started moving from tabular methods (Yang and Gu, 2004; Busoniu et al., 2008) to deep learning methods that can tackle high-dimensional state and action spaces (Tampuu et al.”
MAVEN: Multi-Agent Variational Exploration (2019, background): “In recent years there has been considerable work extending MARL from small discrete state spaces that can be handled by tabular methods [51, 5] to high-dimensional, continuous state spaces that require the use of function approximators [13, 28, 39].”
Multi-Agent Common Knowledge Reinforcement Learning (2018, background): “Related Work Multi-agent reinforcement learning (MARL) has been studied extensively in small environments (Busoniu, Babuska, and De Schutter, 2008; Yang and Gu, 2004), but scaling it to large state spaces or many agents has proved problematic.”
Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems (2012, background): “Various RL algorithms have been proposed in the literature for ILs and fully-cooperative MAS.”
Multi-agent deep reinforcement learning: a survey (2021, background): “Yang and Gu (2004) reviewed algorithms and pointed out that the main difficulty lies in the generalization to continuous action and state spaces and in the scaling to many agents.”
Deep-Learned Collision Avoidance Policy for Distributed Multiagent Navigation (2016, methodology): “In this paper, we use an end-to-end framework to learn a collision-avoidance policy which is robust to the imperfect sensing and requires no inter-agent communication, and thus is still fully distributed.”
Hysteretic q-learning :an algorithm for decentralized reinforcement learning in cooperative multi-agent teams (2007, background): “Learning in multi-agent systems (MAS) are a field of study of growing interest in a wide variety of domains, and especially in multi-robot systems [1].”
Reinforcement learning in robotic applications: a comprehensive survey (2021, methodology): “Effectiveness of MARL algorithm has been tested on applications like resource management, automated marketing, robotics, distributed control, robot soccer game, telecommunications, etc (Zhang et al. 2016; Zhou et al. 2016; Bu et al. 2008; Yang and Gu 2004; Duan et al. 2012; Madden and Howley 2004).”