Table of ContentsSelf-Improving Reactive Agents Based On Reinforcement Learning, Planning and Teaching Overview Introduction Reinforcement Learning Frameworks Reinforcement learning frameworks AHC-learning: Framework AHCON AHC-Learning: Framework AHCON Q-Learning: Framework QCON Q-Learning: Framework QCON Experience Replay Action Models Framework AHCON-M Framework QCON-M Teaching: Frameworks AHCON-T and QCON-T The test environment The Learning Agents Input Representation Output Representation Action Models Prevention of over-training Experimental Results (Global Representation) Experimental Results (Local Representation) QCON-T results Discussion Limitations Conclusions |
Author: Jonathon Edwin Marjamaa
Notes By: Jim Ries Email: JimR@acm.org Home Page: http://riesj.hmi.missouri.edu |