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The successful training of neural networks typically involves careful andtime consuming hyperparameter tuning. Population Based Training (PBT) hasrecently been proposed to automate this process. PBT trains a population ofneural networks concurrently, frequently mutating their hyperparametersthroughout their training. However, the decision mechanisms of PBT are greedyand favour short-term improvements which can, in some cases, lead to poorlong-term performance. This paper presents Faster Improvement Rate PBT (FIREPBT) which addresses this problem. Our method is guided by an assumption: giventwo neural networks with similar performance and training with similarhyperparameters, the network showing the faster rate of improvement will leadto a better final performance. Using this, we derive a novel fitness metric anduse it to make some of the population members focus on long-term performance.Our experiments show that FIRE PBT is able to outperform PBT on the ImageNetbenchmark and match the performance of networks that were trained with ahand-tuned learning rate schedule. We apply FIRE PBT to reinforcement learningtasks and show that it leads to faster learning and higher final performancethan both PBT and random hyperparameter search.

Faster Improvement Rate Population Based Training