73 search results - page 1 / 15 » Learning multi-agent state space representations |
AIIDE
2006
13 years 6 months ago
2006
Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In high-dimensional and continuous domains, ti...
13
Voted
ATAL
2004
Springer
13 years 10 months ago
2004
Springer
In multi-agent MDPs, it is generally necessary to consider the joint state space of all agents, making the size of the problem and the solution exponential in the number of agents...
ATAL
2010
Springer
13 years 6 months ago
2010
Springer
This paper describes an algorithm, called CQ-learning, which learns to adapt the state representation for multi-agent systems in order to coordinate with other agents. We propose ...
ATAL
2009
Springer
13 years 11 months ago
2009
Springer
Multi-Agent Reinforcement Learning (MARL) algorithms suffer from slow convergence and even divergence, especially in largescale systems. In this work, we develop an organization-b...
FLAIRS
2008
13 years 7 months ago
2008
Current studies have demonstrated that the representational power of predictive state representations (PSRs) is at least equal to the one of partially observable Markov decision p...