Uniformly Conservative Exploration in Reinforcement Learning
This addresses the problem of safe exploration in RL for real-world applications like medical treatment, though it is incremental as it builds on existing UCB methods with a new constraint.
The paper tackles the challenge of avoiding harmful exploration in reinforcement learning by proposing a constraint that ensures uniformly outperforming a conservative policy within a per-episode budget, and it demonstrates this approach on sepsis and HIV treatment tasks with good performance compared to baselines.
A key challenge to deploying reinforcement learning in practice is avoiding excessive (harmful) exploration in individual episodes. We propose a natural constraint on exploration -- \textit{uniformly} outperforming a conservative policy (adaptively estimated from all data observed thus far), up to a per-episode exploration budget. We design a novel algorithm that uses a UCB reinforcement learning policy for exploration, but overrides it as needed to satisfy our exploration constraint with high probability. Importantly, to ensure unbiased exploration across the state space, our algorithm adaptively determines when to explore. We prove that our approach remains conservative while minimizing regret in the tabular setting. We experimentally validate our results on a sepsis treatment task and an HIV treatment task, demonstrating that our algorithm can learn while ensuring good performance compared to the baseline policy for every patient; the latter task also demonstrates that our approach extends to continuous state spaces via deep reinforcement learning.