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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/ehtesam3154/biomarl

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Biological Pathway Guided Gene Selection Through Collaborative Reinforcement Learning

https://github.com/eslam211/conservative-and-distributional-marl

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Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning

https://github.com/eugenevinitsky/sequential_social_dilemma_games

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Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

https://github.com/facebookresearch/CollaQ

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Multi-Agent Collaboration via Reward Attribution Decomposition

https://github.com/facebookresearch/Hanabi_SAD

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Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning

https://github.com/facebookresearch/M3RL

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M$^3$RL: Mind-aware Multi-agent Management Reinforcement Learning

https://github.com/facebookresearch/benchmarl

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BenchMARL: Benchmarking Multi-Agent Reinforcement Learning

https://github.com/facebookresearch/decrypto

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The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind

https://github.com/farama-foundation/momaland

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MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning

https://github.com/farquhar13/rl_transmission_control

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Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement Learning

https://github.com/fbredell/marl_artificial_conventions_hanabi

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Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi

https://github.com/flairox/jaxmarl

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JaxMARL: Multi-Agent RL Environments and Algorithms in JAX

https://github.com/geek-ai/MAgent

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MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence

https://github.com/giangbang/strong-pareto-marl

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Toward Finding Strong Pareto Optimal Policies in Multi-Agent Reinforcement Learning

https://github.com/gjp1203/nui_in_madrl

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Negative Update Intervals in Deep Multi-Agent Reinforcement Learning

https://github.com/gml16/rl-medical

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Communicative Reinforcement Learning Agents for Landmark Detection in Brain Images

https://github.com/gt-star-lab/jaxrobotarium

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JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutes

https://github.com/gundoganalperen/DIRAL

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Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V Communication

https://github.com/gyz1997/MARL-for-Stackelberg-game

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Balancing Performance and Cost for Two-Hop Cooperative Communications: Stackelberg Game and Distributed Multi-Agent Reinforcement Learning

https://github.com/hafezgh/risk-sensitive-marl-namg

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Risk-Sensitive Multi-Agent Reinforcement Learning in Network Aggregative Markov Games

https://github.com/hahayonghuming/VDACs

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Value-Decomposition Multi-Agent Actor-Critics

https://github.com/hanhananderson/pac-marl

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PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement Learning

https://github.com/harry67hu/madps

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Measuring Policy Distance for Multi-Agent Reinforcement Learning

https://github.com/harvard-cns/teal

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Teal: Learning-Accelerated Optimization of WAN Traffic Engineering

https://github.com/hbayerlein/uav_data_harvesting

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Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning

https://github.com/hegasz/large-legislative-models

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Large Legislative Models: Towards Efficient AI Policymaking in Economic Simulations

https://github.com/hewlettpackard/dc-rl

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SustainDC: Benchmarking for Sustainable Data Center Control

https://github.com/hhhusiyi-monash/UPDeT

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UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers

https://github.com/hijkzzz/pymarl2

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Rethinking the Implementation Matters in Cooperative Multi-Agent Reinforcement Learning

https://github.com/hisato-komatsu/marl_esn_pedestrian

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Multi-agent reinforcement learning using echo-state network and its application to pedestrian dynamics

https://github.com/hiyuchang/leadq

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Learn How to Query from Unlabeled Data Streams in Federated Learning

https://github.com/hoenchioma/depaint

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DePAint: A Decentralized Safe Multi-Agent Reinforcement Learning Algorithm considering Peak and Average Constraints

https://github.com/hsvgbkhgbv/SQDDPG

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Shapley Q-value: A Local Reward Approach to Solve Global Reward Games

https://github.com/hsvgbkhgbv/shapley-q-learning

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SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning

https://github.com/huawei-noah/SMARTS

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SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving

https://github.com/hyeon1996/fox

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FoX: Formation-aware exploration in multi-agent reinforcement learning

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