Papers with Code 아카이브

Multi-agent Reinforcement Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 3
보존된 평가 기록: 3

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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/SamuelMallick/dmpcrl-concept

공식 코드

Multi-Agent Reinforcement Learning via Distributed MPC as a Function Approximator

https://github.com/SimonLucas/ntbea

공식 코드

Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best

https://github.com/Skylark0924/Gamma_Reward

공식 코드

Learning Scalable Multi-Agent Coordination by Spatial Differentiation for Traffic Signal Control

https://github.com/SocialDeductionLLM/SocialDeductionLLM

공식 코드

Training Language Models for Social Deduction with Multi-Agent Reinforcement Learning

https://github.com/Sonkyunghwan/QTRAN

공식 코드

QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

https://github.com/THUNLP-MT/CollabUIAgents

공식 코드

Enhancing Language Multi-Agent Learning with Multi-Agent Credit Re-Assignment for Interactive Environment Generalization

https://github.com/TIERS/NoisyKukaReacher

공식 코드

Towards Closing the Sim-to-Real Gap in Collaborative Multi-Robot Deep Reinforcement Learning

https://github.com/TorchCraft/TorchCraftAI

공식 코드

A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning

https://github.com/UMich-ML-Group/RL-Ridesharing

공식 코드

Efficient Ridesharing Dispatch Using Multi-Agent Reinforcement Learning

https://github.com/UTS-AAi/autoweka

공식 코드

AutoWeka4MCPS-AVATAR: Accelerating Automated Machine Learning Pipeline Composition and Optimisation

https://github.com/Unity-Technologies/ml-agents

공식 코드

On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning

https://github.com/Vvrep/MASTER-electric_vehicle_charging_recommendation

공식 코드

Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning

https://github.com/Wei9711/GACG

공식 코드

Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning

https://github.com/Wei9711/LTSCG

공식 코드

Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning

https://github.com/WentseChen/Soft-QMIX

공식 코드

Soft-QMIX: Integrating Maximum Entropy For Monotonic Value Function Factorization

https://github.com/YuhangSong/Arena-Baselines

공식 코드

Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence

https://github.com/YuhangSong/Arena-BuildingToolkit

공식 코드

Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence

https://github.com/aailab-kaist/trama

공식 코드

Trajectory-Class-Aware Multi-Agent Reinforcement Learning

https://github.com/acastagn/EF-OnTL

공식 코드

Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning

https://github.com/ai4co/camp

공식 코드

CAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems

https://github.com/ai4co/eph-mapf

공식 코드

Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding

https://github.com/aicenter/openspiel_reproductions

공식 코드

Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Report

https://github.com/ailabdsunipi/pymarlzooplus

공식 코드

An Extended Benchmarking of Multi-Agent Reinforcement Learning Algorithms in Complex Fully Cooperative Tasks

https://github.com/airi-institute/pogema

공식 코드

POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfinding

https://github.com/alex-petrenko/sample-factory

공식 코드

Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

https://github.com/algorithmic-alignment-lab/contracts

공식 코드

Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL

https://github.com/aloriosa/srmt

공식 코드

SRMT: Shared Memory for Multi-agent Lifelong Pathfinding

https://github.com/alshedivat/lola

공식 코드

Learning with Opponent-Learning Awareness

https://github.com/andreyrisukhin/colorgrid

공식 코드

ColorGrid: A Multi-Agent Non-Stationary Environment for Goal Inference and Assistance

https://github.com/anndvision/vsop

공식 코드

ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages

https://github.com/anonymous-iclr22/trust-region-in-multi-agent-reinforcement-learning

공식 코드

Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning

https://github.com/apexrl/AORPO

공식 코드

Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts

https://github.com/arana-initiatives/gov-rek-marls

공식 코드

GOV-REK: Governed Reward Engineering Kernels for Designing Robust Multi-Agent Reinforcement Learning Systems

https://github.com/asatheesh6561/mappo-lce

공식 코드

A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control

https://github.com/asl-epfl/decpomdp_policy_evaluation_w-belief_sharing

공식 코드

Policy Evaluation in Decentralized POMDPs with Belief Sharing

https://github.com/awarelab/seed_rl

공식 코드

Off-Policy Correction For Multi-Agent Reinforcement Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.