sensory data as input and no prior knowledge of the rules of the games in deepmind again captured the imagination of the machine learning community--this time the deepmind reinforcement learning-based ai agent alphago beat lee sedolone of the world' best go players these successes have cemented reinforcement learning as mainstream ai topic todaymachine learning researchers are applying reinforcement learning to solve many different types of problems includingstock market tradingin which the agent buys and sells (actionsand receives profits or losses (rewardsin return video games and board gamesin which the agent makes game decisions (actionsand wins or loses (rewardsself-driving carsin which the agent directs the vehicle (actionsand either stays on course or crashes (rewardsmachine controlin which the agent moves about its environment (actionsand either completes the course or fails (rewardsin the simplest reinforcement learning problemswe have finite problem--with finite number of states of the environmenta finite number of actions that are possible at any given state of the environmentand finite number of rewards the action taken by the agent given the current state of the environment determines the next stateand the agent' goal is to maximize its long-term reward this family of problems is known as finite markov decision processes howeverin the real worldthings are not so simple--the reward is unknown and dynamic rather than known and static to help discover this unknown reward function and approximate it as best as possiblewe can apply unsupervised learning using this approximated reward functionwe can apply reinforcement learning solutions to increase the cumulative reward over time semisupervised learning even though supervised learning and unsupervised learning are two distinct major branches of machine learningthe algorithms from each branch can be mixed together as part of machine learning pipeline typicallythis mix of supervised and unsupervised is used when we want to take full advantage of the few labels that we have or when we want to find newyet unknown patterns from unlabeled data in pipeline refers to system of machine learning solutions that are applied in succession to achieve larger objective unsupervised learning in the machine learning ecosystem