Distributed learning for large-scale planning under uncertainty problems with heterogeneous teams

This paper considers the problem of multiagent sequential decision making under uncertainty and incomplete knowledge of the state transition model. A distributed learning framework, where each agent learns an individual model and shares the results with the team, is proposed. The challenges associated with this approach include choosing the model representation for each agent and how to effectively share these representations under limited communication. A decentralized extension of the model learning scheme based on the Incremental Feature Dependency Discovery (Dec-iFDD) is presented to address the distributed learning problem. The representation selection problem is solved by leveraging iFDD's property of adjusting the model complexity based on the observed data. The model sharing problem is addressed by having each agent rank the features of their representation based on the model reduction error and broadcast the most relevant features to their teammates. The algorithm is tested on the multiagent block building and the persistent search and track missions. The results show that the proposed distributed learning scheme is particularly useful in heterogeneous learning setting, where each agent learns significantly different models. The algorithms developed here are validated on a large-scale persistent search and track flight test with mixed real/virtual agents.