MADRL, Reinforcement Learning, Multi-Agent, MARL, Communication, Centralized Training and Decentralized Execution
정보 : MADRL 슬로우 페이퍼
참가자 : 강효림, 김예찬, 노원종, 박규봉, 배영민, 안홍일, 양홍선, 정재현
퍼실 : 김예찬
Multiagent Cooperation and Competition with Depp Reinforcement Learning
Learning to Communicate to Solve Riddles with Deep distributed Recurrent Q-Network
Deep Reinforcement Learning from Self-Play in Imperfect-Information
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
Learning Multiagent Communication with Backpropagation
Cooperative Multi-Agent Control using Deep Reinforcement Learning
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
Multi-agent Reinforcement Learning in Sequencial Social Dilemma
Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence
Emergence of Grounded Language in Multi-Agent Populations Games
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequence of Symbols
Multi-Agent Cooperation and the Emergence of (Natural) Language(1612)
Counterfactual Multi-Agent Policy Gradients
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Learning with Opponent-Learning Awareness
Emergence Complexity via Multi-Agent Competition
VAIN: Attentional Multi-agent Predictive Modeling(1706)
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Learning Attentional Communication for Multi-Agent Cooperation
Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input
Emergence Communication through Negotiation
Learning Policy Representation in Multiagent Systems
TarMAC: Targeted Multi-Agent Communication