https://github.com/awni00/decentralized-marl-general-cts-spaces
공식 코드Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic Games
Papers with Code 아카이브
Multi-agent Reinforcement Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/awslabs/sagemaker-battlesnake-ai
공식 코드Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loop
https://github.com/azaddeihim/matwm
공식 코드Transformer World Model for Sample Efficient Multi-Agent Reinforcement Learning
https://github.com/baicenxiao/arel
공식 코드Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learning
https://github.com/baicenxiao/shaping-advice
공식 코드https://github.com/baimingc/delay-aware-MARL
공식 코드Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive Environments
https://github.com/bancaditalia/ABCredit.jl
공식 코드https://github.com/baoqianwang/iros22_darl1n
공식 코드https://github.com/bassamlab/sigmarl
공식 코드https://github.com/benedekrozemberczki/shapley
공식 코드https://github.com/bigai-ai/adasociety
공식 코드AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Making
https://github.com/binary-husky/unreal-map
공식 코드Unreal-MAP: Unreal-Engine-Based General Platform for Multi-Agent Reinforcement Learning
https://github.com/blinodelka/Multiagent-Communication-Learing-in-Networks
공식 코드Reinforcement Communication Learning in Different Social Network Structures
https://github.com/buaawgj/tace
공식 코드Adaptive trajectory-constrained exploration strategy for deep reinforcement learning
https://github.com/bupt-antlab/pepcrl-mvp
공식 코드Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle Pursuit
https://github.com/camel-ai/vlm-play-starcraft2
공식 코드https://github.com/canyizl/tpa-for-avc
공식 코드Temporal Prototype-Aware Learning for Active Voltage Control on Power Distribution Networks
https://github.com/carolinewang01/dm2
공식 코드DM$^2$: Decentralized Multi-Agent Reinforcement Learning for Distribution Matching
https://github.com/carolinewang01/naht
공식 코드https://github.com/cas-lab-munich/sigmarl
공식 코드https://github.com/cathyhxh/ctds
공식 코드CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning
https://github.com/cesaremagnetti/automaticusnavigation
공식 코드Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style Transfer
https://github.com/cgchrfchscyrh/MixedTrafficControl
공식 코드Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning
https://github.com/cgrivera/ai-arena
공식 코드The AI Arena: A Framework for Distributed Multi-Agent Reinforcement Learning
https://github.com/chandar-lab/adaptive-hanabi
공식 코드Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi
https://github.com/chandar-lab/coe
공식 코드Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning
https://github.com/chandar-lab/r3d2-a-generalist-hanabi-agent
공식 코드https://github.com/chaovven/SMIX
공식 코드SMIX($λ$): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement Learning
https://github.com/chaovven/maab
공식 코드A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
https://github.com/chauncygu/multi-agent-constrained-policy-optimisation
공식 코드https://github.com/chenyiqun/mmoa-rag
공식 코드Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning
https://github.com/chrfabian/learning_sparse_gmfgs
공식 코드https://github.com/christopher-hsu/scalableMARL
공식 코드Scalable Reinforcement Learning Policies for Multi-Agent Control
https://github.com/cirrick/multi_uav_data_harvesting
공식 코드Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT Networks
https://github.com/cityflow-project/CityFlow
공식 코드CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
https://github.com/cjdjr/t-maac
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.