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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/tseyde/decqn

공식 코드

Solving Continuous Control via Q-learning

https://github.com/tu-delft-ai-energy-lab/marl-idr-multi-agent-reinforcement-learning-for-incentive-based-residential-demand-response

공식 코드

MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response

https://github.com/uoe-agents/epymarl

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Deep Reinforcement Learning for Multi-Agent Interaction

https://github.com/uoe-agents/lb-foraging

공식 코드

Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning

https://github.com/uoe-agents/mate

공식 코드

Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning

https://github.com/uoe-agents/medoe

공식 코드

Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition

https://github.com/uoe-agents/pressureplate

공식 코드

Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization

https://github.com/uoe-agents/revisiting-maddpg

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Revisiting the Gumbel-Softmax in MADDPG

https://github.com/uoe-agents/robotic-warehouse

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Deep Reinforcement Learning for Multi-Agent Interaction

https://github.com/uoe-agents/seac

공식 코드

Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning

https://github.com/uoe-agents/seps

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Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing

https://github.com/uoe-agents/smaclite

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SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning

https://github.com/uoe-agents/task-assignment-robotic-warehouse

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Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers

https://github.com/ustchlsong/ma2cl

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MA2CL:Masked Attentive Contrastive Learning for Multi-Agent Reinforcement Learning

https://github.com/ut-amrl/graph_navigation

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Decentralized Social Navigation with Non-Cooperative Robots via Bi-Level Optimization

https://github.com/utiasDSL/gym-pybullet-drones

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Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control

https://github.com/vcis-wangchenxu/mrdg

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Generalizable Agent Modeling for Agent Collaboration-Competition Adaptation with Multi-Retrieval and Dynamic Generation

https://github.com/veds12/hecogrid

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Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning

https://github.com/vicgalle/data-sharing

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Data sharing games

https://github.com/victoryxl/replenishmentenv

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A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory Management

https://github.com/waterhorse1/nac

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Neural Auto-Curricula

https://github.com/wendelinboehmer/dcg

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Deep Coordination Graphs

https://github.com/wenshuaizhao/spmarl

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Learning Progress Driven Multi-Agent Curriculum

https://github.com/wingsweihua/colight

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CoLight: Learning Network-level Cooperation for Traffic Signal Control

https://github.com/wjh720/QPLEX

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QPLEX: Duplex Dueling Multi-Agent Q-Learning

https://github.com/wonjoon-yun/quantum-multi-agent-reinforcement-learning

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Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design

https://github.com/woodoxen/tactics2d

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Tactics2D: A Highly Modular and Extensible Simulator for Driving Decision-making

https://github.com/wu-duanduan/ctfde_mpc

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Multi-Agent Reinforcement Learning-Based UAV Pathfinding for Obstacle Avoidance in Stochastic Environment

https://github.com/wudiymy/dqmix

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MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning

https://github.com/wujiduan/zero-sum-lq-games

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Learning Zero-Sum Linear Quadratic Games with Improved Sample Complexity and Last-Iterate Convergence

https://github.com/wuxiyang1996/iPLAN

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iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning

https://github.com/xbgit/FaultTolerance_AACFT

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Towards Fault Tolerance in Multi-Agent Reinforcement Learning

https://github.com/xiaxiaoguang/lightaircraftgame

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Light Aircraft Game : Basic Implementation and training results analysis

https://github.com/xizeroplus/marl-embedding

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Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement Learning

https://github.com/xmu-rl-3dv/riskq

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RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization

https://github.com/xwanghan/carbon-simulator

공식 코드

Carbon Market Simulation with Adaptive Mechanism Design

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