Papers with Code 아카이브

Multi-agent Reinforcement Learning

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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/lich14/CDS

Celebrating Diversity in Shared Multi-Agent Reinforcement Learning

https://github.com/ling-pan/res

The StarCraft Multi-Agent Challenge

https://github.com/liyang619/cole-platform

Collaborating with Humans without Human Data

https://github.com/longtermrisk/marltoolbox

Learning with Opponent-Learning Awareness

https://github.com/madhur-tandon/RL-Project

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/madras-simulator/Multi-Agent-Particle-Environment

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/mahi97/XQMIX

The StarCraft Multi-Agent Challenge

https://github.com/marwanihab/RL_Tag_Game

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/marwanihab/RL_Testing_Noise_ASRN

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/matteokarldonati/Counterfactual-Multi-Agent-Policy-Gradients

Counterfactual Multi-Agent Policy Gradients

https://github.com/mauricemager/multiagent-robot

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/maximecohen2/conception_solution_appli_ia_i4_tp_research

The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition

https://github.com/mit-ll/hanabi_anyplay

Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning

https://github.com/mjiang9/_rlcard

RLCard: A Toolkit for Reinforcement Learning in Card Games

https://github.com/mjiang9/rlcard-copy

RLCard: A Toolkit for Reinforcement Learning in Card Games

https://github.com/mlr-org/mlr

mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions

https://github.com/morning9393/HAPPO-HATRPO

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/mwalton/openspiel_benchmarks

Multi-agent Reinforcement Learning in OpenSpiel: A Reproduction Report

https://github.com/ngoodger/nle-language-wrapper

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

https://github.com/nju-rl/acorm

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/openai/maddpg

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/opendilab/DI-engine

Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/opendilab/DI-engine/blob/main/ding/policy/collaq.py

Multi-Agent Collaboration via Reward Attribution Decomposition

https://github.com/opendilab/DI-engine/blob/main/ding/policy/coma.py

Counterfactual Multi-Agent Policy Gradients

https://github.com/opendilab/DI-engine/blob/main/ding/policy/qmix.py

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

https://github.com/opendilab/DI-engine/blob/main/ding/policy/qtran.py

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

https://github.com/osilab-kaist/smac_plus

The StarCraft Multi-Agent Challenge

https://github.com/oxwhirl/comix

FACMAC: Factored Multi-Agent Centralised Policy Gradients

https://github.com/oxwhirl/facmac

The StarCraft Multi-Agent Challenge

https://github.com/parnika31/MACAAC

Actor-Attention-Critic for Multi-Agent Reinforcement Learning

https://github.com/pervasive-ai-lab/nle-language-wrapper

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

https://github.com/petsol/MultiAgentCooperation_UnityAgent_MADDPG_Udacity

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/philtabor/Multi-Agent-Deep-Deterministic-Policy-Gradients

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/pr-shukla/maddpg-keras

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/proroklab/vectorizedmultiagentparticlesimulator

SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planning

https://github.com/q275212/rlcard

RLCard: A Toolkit for Reinforcement Learning in Card Games

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