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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 701–710 of 1718 papers

TitleStatusHype
How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games—0
How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning—0
Individual specialization in multi-task environments with multiagent reinforcement learners—0
Inducing Cooperative behaviour in Sequential-Social dilemmas through Multi-Agent Reinforcement Learning using Status-Quo Loss—0
Human Machine Co-adaption Interface via Cooperation Markov Decision Process System—0
Human-Machine Dialogue as a Stochastic Game—0
Hybrid Information-driven Multi-agent Reinforcement Learning—0
Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning—0
Inducing Stackelberg Equilibrium through Spatio-Temporal Sequential Decision-Making in Multi-Agent Reinforcement Learning—0
Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents—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