Massachusetts Institute of Technology

Dhaval Adjodah

Alex Paul Pentland

Yan Leng

Daniel Calacci

Abhimanyu Dubey

Esteban Moro

Peter Krafft

In some implementations of this invention, the performance of a network of reinforcement learning agents is maximized by optimizing the communication topology between the agents for the communication of gradients, weights or rewards. For instance, a sparse Erdos-Renyi network may be employed, and network density may be selected in such a way as to maximize reachability and to minimize homogeneity. In some cases, a sparse network topology is employed for massively distributed learning, such as across entire fleets of autonomous vehicles or mobile phones that learn from each other instead of requiring a master to coordinate learning.

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Cambridge, MA, United States of America

Dhaval Adjodah