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Methods/MADDPG

MADDPG

Reinforcement LearningIntroduced 200036 papers
Source Paper

Description

MADDPG, or Multi-agent DDPG, extends DDPG into a multi-agent policy gradient algorithm where decentralized agents learn a centralized critic based on the observations and actions of all agents. It leads to learned policies that only use local information (i.e. their own observations) at execution time, does not assume a differentiable model of the environment dynamics or any particular structure on the communication method between agents, and is applicable not only to cooperative interaction but to competitive or mixed interaction involving both physical and communicative behavior. The critic is augmented with extra information about the policies of other agents, while the actor only has access to local information. After training is completed, only the local actors are used at execution phase, acting in a decentralized manner.

Papers Using This Method

Fully-Decentralized MADDPG with Networked Agents2025-03-09Cooperative Multi-Agent Deep Reinforcement Learning in Content Ranking Optimization2024-08-08An Initial Introduction to Cooperative Multi-Agent Reinforcement Learning2024-05-10Combinatorial Client-Master Multiagent Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing2024-02-18Privacy Preserving Multi-Agent Reinforcement Learning in Supply Chains2023-12-09Adaptive Resource Management for Edge Network Slicing using Incremental Multi-Agent Deep Reinforcement Learning2023-10-26Safe Hierarchical Reinforcement Learning for CubeSat Task Scheduling Based on Energy Consumption2023-09-21Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle Pursuit2023-06-08Reinforcement Learning With Reward Machines in Stochastic Games2023-05-27Revisiting the Gumbel-Softmax in MADDPG2023-02-23On Multi-Agent Deep Deterministic Policy Gradients and their Explainability for SMARTS Environment2023-01-20Multiagent Reinforcement Learning Based on Fusion-Multiactor-Attention-Critic for Multiple-Unmanned-Aerial-Vehicle Navigation Control2022-10-10A New Approach to Training Multiple Cooperative Agents for Autonomous Driving2022-09-05Two-Hop Age of Information Scheduling for Multi-UAV Assisted Mobile Edge Computing: FRL vs MADDPG2022-06-19Balancing Profit, Risk, and Sustainability for Portfolio Management2022-06-06MA-Dreamer: Coordination and communication through shared imagination2022-04-10Decision-making of Emergent Incident based on P-MADDPG2022-03-19Learning to Infer Belief Embedded Communication2022-03-15Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method2021-10-31Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning2021-09-23