https://github.com/shariqiqbal2810/MAAC
공식 코드Actor-Attention-Critic for Multi-Agent Reinforcement Learning
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/shariqiqbal2810/Multi-Explore
공식 코드Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning
https://github.com/shariqiqbal2810/REFIL
공식 코드Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
https://github.com/shariqiqbal2810/alma
공식 코드https://github.com/sharlinu/marc
공식 코드Investigating Relational State Abstraction in Collaborative MARL
https://github.com/shengchao-y/maaac-driving
공식 코드Agent-Agnostic Centralized Training for Decentralized Multi-Agent Cooperative Driving
https://github.com/sigmabm/mace
공식 코드Settling Decentralized Multi-Agent Coordinated Exploration by Novelty Sharing
https://github.com/sihongho/robust_marl_with_state_uncertainty
공식 코드Robust Multi-Agent Reinforcement Learning with State Uncertainty
https://github.com/silent-zebra/pola
공식 코드https://github.com/sisl/DICG
공식 코드Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning
https://github.com/sjtu-marl/malib
공식 코드MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
https://github.com/sjtu-marl/zsc-eval
공식 코드ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordination
https://github.com/smrizvi1/uav_energy_analysis_sdqn
공식 코드Multi-Agent Reinforcement Learning with Action Masking for UAV-enabled Mobile Communications
https://github.com/social-ai-uoft/sorrel
공식 코드Sorrel: A simple and flexible framework for multi-agent reinforcement learning
https://github.com/somnathhazra/uncertainties_marl
공식 코드https://github.com/sophieyueguo/explainable_action_advising
공식 코드Explainable Action Advising for Multi-Agent Reinforcement Learning
https://github.com/spiral-rl/spiral
공식 코드https://github.com/sriram94/matlql
공식 코드Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning
https://github.com/sriram94/multiagentadvisorqlearning
공식 코드https://github.com/ssokota/capi
공식 코드Solving Common-Payoff Games with Approximate Policy Iteration
https://github.com/ssokota/tiny-hanabi
공식 코드Solving Common-Payoff Games with Approximate Policy Iteration
https://github.com/stanfordvl/alignment
공식 코드ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward
https://github.com/steamerlee/masa
공식 코드https://github.com/sumitsk/matrl
공식 코드Learning Transferable Cooperative Behavior in Multi-Agent Teams
https://github.com/sunwoolee0504/wall
공식 코드Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
https://github.com/susanbao/rmarl_code
공식 코드What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?
https://github.com/swarmrl/swarmrl
공식 코드Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learning
https://github.com/tegg89/magnet
공식 코드Deep Multi-Agent Reinforcement Learning with Relevance Graphs
https://github.com/tencent-ailab/hokoff
공식 코드Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning Benchmarks
https://github.com/tencent-ailab/mini-hok
공식 코드Mini Honor of Kings: A Lightweight Environment for Multi-Agent Reinforcement Learning
https://github.com/tencent-ailab/tleague_projpage
공식 코드TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement Learning
https://github.com/tessavdheiden/social_empowerment
공식 코드Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer Empowerment
https://github.com/thanosvlo/MARL-for-Anatomical-Landmark-Detection
공식 코드Multiple Landmark Detection using Multi-Agent Reinforcement Learning
https://github.com/thu-rllab/CFCQL
공식 코드Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning
https://github.com/tobiasbaumann1/Adaptive_Mechanism_Design
공식 코드https://github.com/trustai/certifycmarl
공식 코드Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.