This thesis explores the development of a risk-averse distributional reinforcement learning (DRL) algorithm designed to address the limitations of traditional reinforcement learning (RL) methods in handling uncertain and risk-sensitive environments. We present modifications to the standard C51 algorithm, and propose a risk-averse strategy by introducing the risk-adverse modifications for both loss function and policy selection. Through empirical evaluations conducted across four Atari games, we assess the performance of these modifications in comparison to the original C51 framework. Our results demonstrate that strategic adjustments to the policy can significantly enhance performance by reducing variability. Surprisingly, the modified algorithms achieve higher mean scores for some games. This result could be attributed to the chosen parameters and more data is needed in order to verify these results. Conversely, modifications to the loss function showed mixed results, often failing to improve and sometimes even degrading performance.