Reinforcement Learning, Machine Learning, Policy, Data Science, Artificial Intelligence.
cumulative reward the agent receives by the end of the episode. Since every move in the maze adds -1 to the reward, we can interpret the v-value of 18 as the expected number of steps the agent will have to make until it gets to the terminal state. Policy improvement At first sight, it might sound