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/kclip/bayesian-dt
공식 코드Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning
https://github.com/kinalmehta/epymarl_spectral
공식 코드Effects of Spectral Normalization in Multi-agent Reinforcement Learning
https://github.com/kinalmehta/marl-jax
공식 코드https://github.com/kjboggess/ijcai23
공식 코드Explainable Multi-Agent Reinforcement Learning for Temporal Queries
https://github.com/kkane99/MANGER_code
공식 코드Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning
https://github.com/kkane99/manger
공식 코드Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning
https://github.com/kth-flowai/advanceddeepreinforcementlearning_rbc_control
공식 코드https://github.com/kth-flowai/deepreinforcementlearning_rayleighbenard2d_control
공식 코드https://github.com/kth-flowai/drl_marl_rayleighbenard3d_control
공식 코드Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-Bénard convection
https://github.com/kwai/DouZero
공식 코드DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
https://github.com/lab-work/marl-vsl-controller
공식 코드https://github.com/lamda-bbo/maze
공식 코드Heterogeneous Multi-agent Zero-Shot Coordination by Coevolution
https://github.com/lethanh-96/tinyqmix-mtc
공식 코드TinyQMIX: Distributed Access Control for mMTC via Multi-agent Reinforcement Learning
https://github.com/lijunsun90/pursuitfsc2
공식 코드Toward multi-target self-organizing pursuit in a partially observable Markov game
https://github.com/linkjoker1006/pymarl3
공식 코드https://github.com/liuqh16/mazero
공식 코드https://github.com/liushunyu/cia
공식 코드Contrastive Identity-Aware Learning for Multi-Agent Value Decomposition
https://github.com/liushunyu/opt
공식 코드Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning
https://github.com/liza-tennant/moral_choice_dyadic
공식 코드Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning
https://github.com/locke637/avgm
공식 코드https://github.com/locross93/hypothetical-minds
공식 코드Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language Models
https://github.com/lrhammond/almanac
공식 코드Multi-Agent Reinforcement Learning with Temporal Logic Specifications
https://github.com/ltluttmann/marl4msprp
공식 코드https://github.com/lucascjysdl/deepfreight
공식 코드https://github.com/lucidrains/perceiver-pytorch
공식 코드https://github.com/lucidrains/vector-quantize-pytorch
공식 코드Revisiting Multi-Agent World Modeling from a Diffusion-Inspired Perspective
https://github.com/lxxxxr/cacom
공식 코드Context-aware Communication for Multi-agent Reinforcement Learning
https://github.com/lxxxxr/expocomm
공식 코드Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning
https://github.com/lxxxxr/ices
공식 코드Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning
https://github.com/lxxxxr/kaleidoscope
공식 코드Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning
https://github.com/lych1233/gamma-human-ai-collaboration
공식 코드https://github.com/lyu-xg/on-centralized-critics-in-marl
공식 코드On Centralized Critics in Multi-Agent Reinforcement Learning
https://github.com/mail-ecnu/PICO
공식 코드Multi-Agent Path Finding with Prioritized Communication Learning
https://github.com/manluow/ev-simulator
공식 코드https://github.com/mannanb/diat
공식 코드Interpretable Emergent Language Using Inter-Agent Transformers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.