Sell Your Projects | My Account | Careers | This email address is being protected from spambots. You need JavaScript enabled to view it. | Call: +91 9573777164

Using Learning Classifier Systems to Learn Stochastic Decision Policies

1 1 1 1 1 Rating 4.89 (45 Votes)

PROJECT TITLE :

Using Learning Classifier Systems to Learn Stochastic Decision Policies

ABSTRACT:

To solve reinforcement learning problems, several learning classifier systems (LCSs) are designed to be told state-action value functions through a compact set of maximally general and correct rules. Most of those systems focus primarily on learning deterministic policies by using a greedy action choice strategy. But, in observe, it may be more flexible and fascinating to learn stochastic policies, that can be thought-about as direct extensions of their deterministic counterparts. In this paper, we aim to achieve this goal by extending every rule with a brand new policy parameter. Meanwhile, a new method for adaptive learning of stochastic action selection strategies primarily based on a policy gradient framework has additionally been introduced. Using this technique, we tend to have developed 2 new learning systems, one based on a daily gradient learning technology and the opposite primarily based on a replacement natural gradient learning technique. Both learning systems are evaluated on three different varieties of reinforcement learning issues. The promising performance of the 2 systems clearly shows that LCSs provide a suitable platform for efficient and reliable learning of stochastic policies.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


Using Learning Classifier Systems to Learn Stochastic Decision Policies - 4.9 out of 5 based on 45 votes

Project EnquiryLatest Ready Available Academic Live Projects in affordable prices

Included complete project review wise documentation with project explanation videos and Much More...