𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Networked reinforcement learning

✍ Scribed by Makito Oku; Kazuyuki Aihara

Publisher
Springer Japan
Year
2008
Tongue
English
Weight
433 KB
Volume
13
Category
Article
ISSN
1433-5298

No coin nor oath required. For personal study only.

πŸ“œ SIMILAR VOLUMES

Constructive reinforcement learning
Constructive reinforcement learning
✍ Jose Hernandez-Orallo πŸ“‚ Article πŸ“… 2000 πŸ› John Wiley and Sons 🌐 English βš– 163 KB

This paper presents an operative measure of reinforcement for constructive learning Ε½ . methods, i.e., eager learning methods using highly expressible or universal representation languages. These evaluation tools allow a further insight in the study of the growth of knowledge, theory revision, and a

Distributed reinforcement learning
Distributed reinforcement learning
✍ Gerhard WeiΞ² πŸ“‚ Article πŸ“… 1995 πŸ› Elsevier Science 🌐 English βš– 623 KB
Improving reinforcement learning in stoc
Improving reinforcement learning in stochastic RAM-based neural networks
✍ Alistair Ferguson; Hamid Bolouri πŸ“‚ Article πŸ“… 1996 πŸ› Springer US 🌐 English βš– 250 KB

RAM-based neural networks are designed to be efficiently implemented in hardware. The desire to retain this property influences the training algorithms used, and has led to the use of reinforcement (reward-penalty) learning. An analysis of the reinforcement algorithm applied to RAM-based nodes has s

Two steps reinforcement learning
Two steps reinforcement learning
✍ Fernando FernΓ‘ndez; Daniel Borrajo πŸ“‚ Article πŸ“… 2008 πŸ› John Wiley and Sons 🌐 English βš– 977 KB

When applying reinforcement learning in domains with very large or continuous state spaces, the experience obtained by the learning agent in the interaction with the environment must be generalized. The generalization methods are usually based on the approximation of the value functions used to comp