Abstract: Reinforcement learning (RL) offers the potential for training generally capable agents that can interact autonomously in the real world. However, one key limitation is the brittleness of RL algorithms to core hyperparameters and network architecture choice. Furthermore, non-stationarities such as evolving training data and increased agent complexity mean that different hyperparameters and architectures may be optimal at different points of training. This motivates AutoRL, a class of methods seeking to automate these design choices. One prominent class of AutoRL methods is Population-Based Training (PBT), which have led to impressive performance in several large scale settings. In this paper, we introduce two new innovations in PBT-style methods. First, we employ trust-region based Bayesian Optimization, enabling full coverage of the high-dimensional mixed hyperparameter search space. Second, we show that using a generational approach, we can also learn both architectures and hyperparameters jointly on-the-fly in a single training run. Leveraging the new highly parallelizable Brax physics engine, we show that these innovations lead to dramatic performance gains, significantly outperforming the tuned baseline while learning entire configurations on the fly.
Keywords: reinforcement learning, AutoRL, Bayesian optimization
One-sentence Summary: We present an AutoRL method that tunes both hyperparameters and architectures on the fly with Bayesian optimization, distillation and population-based training.
Track: Main track
Reproducibility Checklist: Yes
Broader Impact Statement: Yes
Paper Availability And License: Yes
Code Of Conduct: Yes
Reviewers: Xingchen Wan, firstname.lastname@example.org
Main Paper And Supplementary Material: pdf
Code And Dataset Supplement: zip
CPU Hours: 0
GPU Hours: 0
TPU Hours: 0