  Many of the recent triumphs in machine learning are dependent on well-tuned
hyperparameters. This is particularly prominent in reinforcement learning (RL)
where a small change in the configuration can lead to failure. Despite the
importance of tuning hyperparameters, it remains expensive and is often done in
a naive and laborious way. A recent solution to this problem is Population
Based Training (PBT) which updates both weights and hyperparameters in a single
training run of a population of agents. PBT has been shown to be particularly
effective in RL, leading to widespread use in the field. However, PBT lacks
theoretical guarantees since it relies on random heuristics to explore the
hyperparameter space. This inefficiency means it typically requires vast
computational resources, which is prohibitive for many small and medium sized
labs. In this work, we introduce the first provably efficient PBT-style
algorithm, Population-Based Bandits (PB2). PB2 uses a probabilistic model to
guide the search in an efficient way, making it possible to discover high
performing hyperparameter configurations with far fewer agents than typically
required by PBT. We show in a series of RL experiments that PB2 is able to
achieve high performance with a modest computational budget.
