A survey of progress on cooperative multi-agent reinforcement learning in open environment
Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …
has made progress in various fields. Specifically, cooperative MARL focuses on training a …
Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement Learning
Being able to harness the power of large datasets for developing cooperative multi-agent
controllers promises to unlock enormous value for real-world applications. Many important …
controllers promises to unlock enormous value for real-world applications. Many important …
Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning
Reinforcement learning (RL) has been widely adopted for controlling and optimizing
complex engineering systems such as next-generation wireless networks. An important …
complex engineering systems such as next-generation wireless networks. An important …
Coordination failure in cooperative offline marl
Offline multi-agent reinforcement learning (MARL) leverages static datasets of experience to
learn optimal multi-agent control. However, learning from static data presents several unique …
learn optimal multi-agent control. However, learning from static data presents several unique …
Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization
Z Liu, Q Lin, C Yu, X Wu, Y Liang, D Li… - arXiv preprint arXiv …, 2024 - arxiv.org
Offline Multi-Agent Reinforcement Learning (MARL) is an emerging field that aims to learn
optimal multi-agent policies from pre-collected datasets. Compared to single-agent case …
optimal multi-agent policies from pre-collected datasets. Compared to single-agent case …
Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning
Offline multi-agent reinforcement learning (MARL) is an exciting direction of research that
uses static datasets to find optimal control policies for multi-agent systems. Though the field …
uses static datasets to find optimal control policies for multi-agent systems. Though the field …
Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and Evaluation
Offline multi-agent reinforcement learning (MARL) is an emerging field with great promise for
real-world applications. Unfortunately, the current state of research in offline MARL is …
real-world applications. Unfortunately, the current state of research in offline MARL is …
Efficient Communication via Self-Supervised Information Aggregation for Online and Offline Multiagent Reinforcement Learning
Utilizing messages from teammates can improve coordination in cooperative multiagent
reinforcement learning (MARL). Previous works typically combine raw messages of …
reinforcement learning (MARL). Previous works typically combine raw messages of …