Towards playing full moba games with deep reinforcement learning
MOBA games, eg, Honor of Kings, League of Legends, and Dota 2, pose grand challenges
to AI systems such as multi-agent, enormous state-action space, complex action control, etc …
to AI systems such as multi-agent, enormous state-action space, complex action control, etc …
Simulation intelligence: Towards a new generation of scientific methods
The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …
computing, where a motif is an algorithmic method that captures a pattern of computation …
Deep reinforcement learning in medical imaging: A literature review
Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …
learns a sequence of actions that maximizes the expected reward, with the representative …
Learning with opponent-learning awareness
Multi-agent settings are quickly gathering importance in machine learning. This includes a
plethora of recent work on deep multi-agent reinforcement learning, but also can be …
plethora of recent work on deep multi-agent reinforcement learning, but also can be …
Autonomous agents modelling other agents: A comprehensive survey and open problems
SV Albrecht, P Stone - Artificial Intelligence, 2018 - Elsevier
Much research in artificial intelligence is concerned with the development of autonomous
agents that can interact effectively with other agents. An important aspect of such agents is …
agents that can interact effectively with other agents. An important aspect of such agents is …
A survey on transfer learning for multiagent reinforcement learning systems
FL Da Silva, AHR Costa - Journal of Artificial Intelligence Research, 2019 - jair.org
Multiagent Reinforcement Learning (RL) solves complex tasks that require coordination with
other agents through autonomous exploration of the environment. However, learning a …
other agents through autonomous exploration of the environment. However, learning a …
多智能体深度强化学习的若干关键科学问题
孙长银， 穆朝絮 - 自动化学报, 2020 - aas.net.cn
强化学习作为一种用于解决无模型序列决策问题的方法已经有数十年的历史,
但强化学习方法在处理高维变量问题时常常会面临巨大挑战. 近年来, 深度学习迅猛发展 …
但强化学习方法在处理高维变量问题时常常会面临巨大挑战. 近年来, 深度学习迅猛发展 …
A scalable privacy-preserving multi-agent deep reinforcement learning approach for large-scale peer-to-peer transactive energy trading
Peer-to-peer (P2P) transactive energy trading has emerged as a promising paradigm
towards maximizing the flexibility value of prosumers' distributed energy resources (DERs) …
towards maximizing the flexibility value of prosumers' distributed energy resources (DERs) …
Scalable coordinated management of peer-to-peer energy trading: A multi-cluster deep reinforcement learning approach
The increasing penetration of small-scale distributed energy resources (DER) has the
potential to support cost-efficient energy balancing in emerging electricity systems, but is …
potential to support cost-efficient energy balancing in emerging electricity systems, but is …
Dop: Off-policy multi-agent decomposed policy gradients
Multi-agent policy gradient (MAPG) methods recently witness vigorous progress. However,
there is a significant performance discrepancy between MAPG methods and state-of-the-art …
there is a significant performance discrepancy between MAPG methods and state-of-the-art …