Reinforcement learning agents are trained in well-defined environments and evaluated under the assumption that the test time conditions match those encountered during training. However, even small changes in the environment’s dynamics can degrade the policy’s performance, even more so in safety critical domains. This work investigates the use of Quantile Regression Deep-Q Networks (QR-DQN) to detect environment shifts by analyzing the uncertainty in return predictions. QR-DQN extends Deep-Q learning by estimating the entire distribution of future returns through quantile regression. We hypothesize that under deterministic settings, the spread of the return distribution, quantified by the inter-quantile range, can determine whether environmental changes took place. The RL agent learns low spread predictions for familiar dynamics, but when deployed in changed environments, the quantile distribution becomes wider. We conduct experiments on the deterministic CartPole-v1 environment by the pole length. We show that the quantile spread is low under small changes, but drastically increases as the dynamics’ shifts diverge more from the training setting. Our results indicate the potential of distributional reinforcement learning to enhance reliability and awareness in deployment scenarios.