Statistically Significant Stopping of Neural Network Training

1 code implementation • 1 Mar 2021 • J. K. Terry, Mario Jayakumar, Kusal De Alwis

The general approach taken when training deep learning classifiers is to save the parameters after every few iterations, train until either a human observer or a simple metric-based heuristic decides the network isn't learning anymore, and then backtrack and pick the saved parameters with the best validation accuracy.

PettingZoo: Gym for Multi-Agent Reinforcement Learning

2 code implementations • NeurIPS 2021 • J. K. Terry, Benjamin Black, Nathaniel Grammel, Mario Jayakumar, Ananth Hari, Ryan Sullivan, Luis Santos, Rodrigo Perez, Caroline Horsch, Clemens Dieffendahl, Niall L. Williams, Yashas Lokesh, Praveen Ravi

This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle ("AEC") games model.

Multi-agent Reinforcement Learning reinforcement-learning +1