Diff-DAC: Distributed Actor-Critic for Multitask Deep Reinforcement Learning

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RODE: Learning Roles to Decompose Multi-Agent Tasks (2020, background): “Another line of research focuses on fully decentralized actor-critic learning (Macua et al., 2017; Zhang et al., 2018; Yang et al., 2018; Cassano et al., 2018; Suttle et al., 2019; Zhang & Zavlanos, 2019).”
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents (2018, background): “One recent work (Macua et al., 2017) for multi-task RL, which is almost concurrent to ours, is also based on the model with networked agents.”
Off-Policy Multi-Agent Decomposed Policy Gradients (2020, background): “Another line of research focuses on fully decentralized actor-critic learning (Macua et al., 2017; Zhang et al., 2018; Yang et al., 2018; Cassano et al., 2018; Suttle et al., 2019; Zhang & Zavlanos, 2019).”
Multiagent Fully Decentralized Value Function Learning With Linear Convergence Rates (2018, methodology): “The regularization term can be particularly useful when the policy evaluation algorithm is used as part of a policy gradient loop (since subsequent policies are expected to have similar value functions and the value of θ learned in one iteration can be used as θp in the next iteration) such as, for ”