A new model-free reinforcement learning framework, Deep-BQRL, has been developed for risk-sensitive decision-making. It extends buffered-quantile learning to neural function approximation, allowing for more general applicability and efficient exploration. The method is compared to existing approaches in experiments, demonstrating its effectiveness in solving asset-selling and slippery FrozenLake problems.