  In real-world tasks, reinforcement learning (RL) agents frequently encounter
situations that are not present during training time. To ensure reliable
performance, the RL agents need to exhibit robustness against worst-case
situations. The robust RL framework addresses this challenge via a worst-case
optimization between an agent and an adversary. Previous robust RL algorithms
are either sample inefficient, lack robustness guarantees, or do not scale to
large problems. We propose the Robust Hallucinated Upper-Confidence RL
(RH-UCRL) algorithm to provably solve this problem while attaining near-optimal
sample complexity guarantees. RH-UCRL is a model-based reinforcement learning
(MBRL) algorithm that effectively distinguishes between epistemic and aleatoric
uncertainty and efficiently explores both the agent and adversary decision
spaces during policy learning. We scale RH-UCRL to complex tasks via neural
networks ensemble models as well as neural network policies. Experimentally, we
demonstrate that RH-UCRL outperforms other robust deep RL algorithms in a
variety of adversarial environments.
