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/ut-amrl/social_gym
SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces
https://github.com/vermashresth/sequential_social_dilemma_games
Multi-agent Reinforcement Learning in Sequential Social Dilemmas
https://github.com/wangxiao5791509/Tracking-with-Deep-Reinforcement-Learning
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam Search
https://github.com/wsjeon/multiagent-particle-envs-maac
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/wsjeon/multiagent-particle-envs-v2
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/xiuyu0000/new_papers_codes/tree/main/mappo
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
https://github.com/xiuyu0000/new_papers_codes/tree/main/qmix
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
https://github.com/xuehy/pytorch-maddpg
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/xukaidi13/Simulation-code
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet
https://github.com/yannbouteiller/gym-airsimdroneracinglab
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/yihenglin97/networked-marl
Multi-Agent Reinforcement Learning in Stochastic Networked Systems
https://github.com/yuant95/SMARTS_VCR
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
https://github.com/zbzhu99/madiff
Plan Better Amid Conservatism: Offline Multi-Agent Reinforcement Learning with Actor Rectification
https://github.com/zfw1226/gym-unrealcv
Coordinate-Aligned Multi-Camera Collaboration for Active Multi-Object Tracking
https://github.com/zoeyuchao/MPE-pytorch
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/zoeyuchao/MPEnew-pytorch
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
https://github.com/zoeyuchao/mappo
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.