Decentralized partially observable Markov decision processes (Dec-POMDPs) provide a
general model for decision-making under uncertainty in decentralized settings, but are …

We address a long-standing open problem of reinforcement learning in decentralized
partially observable Markov decision processes. Previous attempts focussed on different …

Reinforcement Learning (RL) for decentralized partially observable Markov decision
processes (Dec-POMDPs) is lagging behind the spectacular breakthroughs of single-agent …

Optimally solving decentralized partially observable Markov decision processes under either
full or no information sharing received significant attention in recent years. However, little is …

Multi-agent reinforcement learning (MARL) under partial observability has long been
considered challenging, primarily due to the requirement for each agent to maintain a belief …

Decentralized POMDPs provide an expressive framework for multiagent sequential decision
making. However, the complexity of these models--NEXP-Complete even for two agents …

In their seminal work, Nayyar et al.(2013) showed that imperfect information can be
abstracted away from common-payoff games by having players publicly announce their …

State-of-the-art multi-agent policy gradient (MAPG) methods have demonstrated convincing
capability in many cooperative games. However, the exponentially growing joint-action …

Expectation maximization (EM) has recently been shown to be an efficient algorithm for
learning finite-state controllers (FSCs) in large decentralized POMDPs (Dec-POMDPs) …

Recent years have seen significant advances in techniques for optimally solving multiagent
problems represented as decentralized partially observable Markov decision processes …