SOTAVerified

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 1651–1700 of 1718 papers

TitleStatusHype
Multi-Agent Reinforcement Learning with Multi-Step Generative Models—0
A Regulation Enforcement Solution for Multi-agent Reinforcement Learning—0
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning—0
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning—0
Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive ReasoningCode0
Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies—0
The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) CompetitionCode0
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning—0
Multi-agent Reinforcement Learning Embedded Game for the Optimization of Building Energy Control and Power System Planning—0
Optimizing Market Making using Multi-Agent Reinforcement Learning—0
Malthusian Reinforcement Learning—0
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning—0
Communication-Efficient Policy Gradient Methods for Distributed Reinforcement Learning—0
Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents—0
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations—0
Deep Multi-Agent Reinforcement Learning with Relevance GraphsCode0
Emergence of linguistic conventions in multi-agent reinforcement learning—0
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning—0
Multi-Agent Common Knowledge Reinforcement LearningCode0
TarMAC: Targeted Multi-Agent Communication—0
Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks—0
Multi-Agent Actor-Critic with Generative Cooperative Policy Network—0
M^3RL: Mind-aware Multi-agent Management Reinforcement LearningCode0
A Better Baseline for Second Order Gradient Estimation in Stochastic Computation Graphs—0
Learning through Probing: a decentralized reinforcement learning architecture for social dilemmas—0
IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning—0
Prosocial or Selfish? Agents with different behaviors for Contract Negotiation using Reinforcement Learning—0
Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning—0
Coordination-driven learning in multi-agent problem spaces—0
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement LearningCode0
A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising—0
MARL-FWC: Optimal Coordination of Freeway Traffic Control Measures—0
Learning to Share and Hide Intentions using Information RegularizationCode0
Multi-Agent Reinforcement Learning: A Report on Challenges and ApproachesCode0
Learning to Act in Decentralized Partially Observable MDPs—0
Learning Existing Social Conventions via Observationally Augmented Self-Play—0
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization—0
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients—0
On Gradient-Based Learning in Continuous Games—0
Emergent Communication through Negotiation—0
Towards Learning Transferable Conversational Skills using Multi-dimensional Dialogue ModellingCode0
Entropy based Independent Learning in Anonymous Multi-Agent Settings—0
Valuing knowledge, information and agency in Multi-agent Reinforcement Learning: a case study in smart buildings—0
Intent-aware Multi-agent Reinforcement Learning—0
Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising—0
Modeling Others using Oneself in Multi-Agent Reinforcement Learning—0
Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning—0
Learning to Gather without CommunicationCode0
Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet ManagementCode0
Cooperative Multi-Agent Reinforcement Learning for Low-Level Wireless Communication—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