this iterative process continues till it learns enough about its environment to get the desired rewards the main steps of reinforcement learning method are mentioned as follows prepare agent with set of initial policies and strategy observe environment and current state select optimal policy and perform action get corresponding reward (or penalty update policies if needed repeat steps iteratively until agent learns the most optimal policies consider real-world problem of trying to make robot or machine learn to play chess in this case the agent would be the robot and the environment and states would be the chessboard and the positions of the chess pieces suitable reinforcement learning methodology is depicted in figure - figure - reinforcement learningtraining robot to play chess the main steps involved for making the robot learn to play chess is pictorially depicted in figure - this is based on the steps discussed earlier for any reinforcement learning method in factgoogle' deepmind built the alphago ai with components of reinforcement learning to train the system to play the game of go batch learning batch learning methods are also popularly known as offline learning methods these are machine learning methods that are used in end-to-end machine learning systems where the model is trained using all the available training data in one go once training is done and the model completes the process of learningon getting satisfactory performanceit is deployed into production where it predicts outputs for new data samples howeverthe model doesn' keep learning over period of time continuously with the new data once the training is complete the model stops learning thussince the model trains with data in one single batch and it is usually one-time procedurethis is known as batch or offline learning