Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. Book DescriptionReinforcement 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.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |