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...
Συγγραφή απο Οργανισμό/Αρχή: | |
---|---|
Άλλοι συγγραφείς: | |
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.