Artificial Intelligence, Reinforcement Learning, Markov Decision Process, AI, Machine Learning.
environments where the interaction with the agent breaks naturally into subsequences, which are known as episodes. Each episode will end at the terminal state, resetting the environment to its initial state, or starting state. Using the example of Chess-Agent, independently of winning or losing in