Reinforcement Learning for Robots Using Neural Networks
Abstract
Reinforcement learning agents are adaptive, reactive, and self-supervised. The aim of this dissertation is to extend the state of the art of reinforcement learning and enable its applications to complex robot-learning problems. In particular, it focuses on two issues. First, learning from sparse and delayed reinforcement signals is hard and in general a slow process. Techniques for reducing learning time must be devised. Second, most existing reinforcement learning methods assume that the world is a Markov decision process. This assumption is too strong for many robot tasks of interest, This dissertation demonstrates how one can possibly overcome the slow learning problem and tackle non-Markovian environments, making reinforcement learning more practical for realistic robot tasks.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 06, 1993
- Accession Number
- ADA261434
Entities
People
- Long-ji Lin
Organizations
- Carnegie Mellon University