Recent Advances in Reinforcement Learning

Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of popularity in the Artificial Intelligence and Neural Network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Kaelbling, Leslie Pack (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Boston, MA : Springer US, 1996.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • Editorial
  • Efficient Reinforcement Learning through Symbiotic Evolution
  • Linear Least-Squares Algorithms for Temporal Difference Learning
  • Feature-Based Methods for Large Scale Dynamic Programming
  • On the Worst-Case Analysis of Temporal-Difference Learning Algorithms
  • Reinforcement Learning with Replacing Eligibility Traces
  • Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results
  • The Loss from Imperfect Value Functions in Expectation-Based and Minimax-Based Tasks
  • The Effect of Representation and Knowledge on Goal-Directed Exploration with Reinforcement-Learning Algorithms
  • Creating Advice-Taking Reinforcement Learners
  • Technical Note.