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Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

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Showing 801–825 of 1718 papers

TitleStatusHype
Fast Sequence Generation with Multi-Agent Reinforcement Learning—0
Learn as Individuals, Evolve as a Team: Multi-agent LLMs Adaptation in Embodied Environments—0
Learning 3D Navigation Protocols on Touch Interfaces with Cooperative Multi-Agent Reinforcement Learning—0
Fast Multi-Agent Temporal-Difference Learning via Homotopy Stochastic Primal-Dual Optimization—0
Learning a Multi-Agent Controller for Shared Energy Storage System—0
Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning—0
Faster Last-iterate Convergence of Policy Optimization in Zero-Sum Markov Games—0
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces—0
Deep Multi-Agent Reinforcement Learning with Hybrid Action Spaces based on Maximum Entropy—0
Learning Cooperative Multi-Agent Policies with Partial Reward Decoupling—0
Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning—0
Deep Q-Network Based Multi-agent Reinforcement Learning with Binary Action Agents—0
Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning—0
Learning Decentralized Traffic Signal Controllers with Multi-Agent Graph Reinforcement Learning—0
Deep Reinforcement Learning, a textbook—0
Learning Efficient Flocking Control based on Gibbs Random Fields—0
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach—0
Learning Emergence of Interaction Patterns across Independent RL Agents in Multi-Agent Environments—0
Learning Emergent Discrete Message Communication for Cooperative Reinforcement Learning—0
Learning Existing Social Conventions via Observationally Augmented Self-Play—0
Fairness in Multi-agent Reinforcement Learning for Stock Trading—0
Control as Probabilistic Inference as an Emergent Communication Mechanism in Multi-Agent Reinforcement Learning—0
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning—0
A multi-agent reinforcement learning model of reputation and cooperation in human groups—0
Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14—Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15—Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39—Unverified