Sinposis

REINFORCEMENT LEARNING, ONE OF THE MOST ACTIVE RESEARCH AREAS IN ARTIFICIAL INTELLIGENCE, IS A COMPUTATIONAL APPROACH TO LEARNING WHEREBY AN AGENT TRIES TO MAXIMIZE THE TOTAL AMOUNT OF REWARD IT RECEIVES WHILE INTERACTING WITH A COMPLEX, UNCERTAIN ENVIRONMENT. IN REINFORCEMENT LEARNING, RICHARD SUTTON AND ANDREW BARTO PROVIDE A CLEAR AND SIMPLE ACCOUNT OF THE FIELD'S KEY IDEAS AND ALGORITHMS. THIS SECOND EDITION HAS BEEN SIGNIFICANTLY EXPANDED AND UPDATED, PRESENTING NEW TOPICS AND UPDATING COVERAGE OF OTHER TOPICS.