Risk-Sensitive Reinforcement Learning Course Project: Reinforcement Learning (Monisha Jegadeesan, Shreyas Chaudhari) The following risk-measures and algorithms are implemented for a gridworld with error states setting: Risk-Neutral Q-Learning SARSA Risk as the Probability of Entering an Error State R-Learning Risk as the Euclidean Distance from the Nearest Error State S-Learning The mapping between the risk measures, algorithms and code files can be found in algorithm_file_mapping.txt. A detailed report on the analysis of these measures and algorithms can be found here.