Research Papers Library

Search Techniques to Continuous-state Reinforcement Learning

Applying Online Search Techniques to Continuous-State Reinforcement Learning

In this paper, we describe methods for efficiently computing better solutions to control problems in continuous state spaces. We provide algorithms that exploit online search to boost the power of very approximate value functions discovered by traditional reinforcement learning techniques. We examine local searches, where the agent performs a finite-depth lookahead search, and global searches, where the agent performs a search for a trajectory all the way from the current state to a goal state.

Download PDF

AOFIRS

World's leading professional association of Internet Research Specialists - We deliver Knowledge, Education, Training, and Certification in the field of Professional Online Research. The AOFIRS is considered a major contributor in improving Web Search Skills and recognizes Online Research work as a full-time occupation for those that use the Internet as their primary source of information.

Get Exclusive Research Tips in Your Inbox

Receive Great tips via email, enter your email to Subscribe.