An Overview of MAXQ Hierarchical Reinforcement Learning

Authors:
Thomas G. Dietterich
Affiliations:
-
Venue:
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
Year:
2000

Citing 13
Cited 7

Transfer of Learning by Composing Solutions of Elemental Sequential Tasks

Machine Learning
Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time

Machine Learning
Temporal difference learning and TD-Gammon

Communications of the ACM
Reinforcement learning with hierarchies of machines

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Convergence Results for Single-Step On-PolicyReinforcement-Learning Algorithms

Machine Learning
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Neuro-Dynamic Programming

Neuro-Dynamic Programming
Feudal Reinforcement Learning

Advances in Neural Information Processing Systems 5, [NIPS Conference]
Decomposition Techniques for Planning in Stochastic Domains

Decomposition Techniques for Planning in Stochastic Domains
Between MOPs and Semi-MOP: Learning, Planning & Representing Knowledge at Multiple Temporal Scales

Between MOPs and Semi-MOP: Learning, Planning & Representing Knowledge at Multiple Temporal Scales
Hierarchical control and learning for markov decision processes

Hierarchical control and learning for markov decision processes
Hierarchical reinforcement learning with the MAXQ value function decomposition

Journal of Artificial Intelligence Research
A reinforcement learning approach to job-shop scheduling

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2

Evolutionary Meta Compilation: Evolving Programs Using Real World Engineering Tools

ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
Generating adaptive route instructions using hierarchical reinforcement learning

SC'10 Proceedings of the 7th international conference on Spatial cognition
Spatially-aware dialogue control using hierarchical reinforcement learning

ACM Transactions on Speech and Language Processing (TSLP)
Abstraction and generalization in reinforcement learning: a summary and framework

ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
An interactive humanoid robot exhibiting flexible sub-dialogues

NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstration Session
Undesired state-action prediction in multi-agent reinforcement learning for linked multi-component robotic system control

Information Sciences: an International Journal
Hierarchical Social Network Analysis Using a Multi-Agent System: A School System Case

International Journal of Agent Technologies and Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Reinforcement learning addresses the problem of learning optimal policies for sequential decision-making problems involving stochastic operators and numerical reward functions rather than the more traditional deterministic operators and logical goal predicates. In many ways, reinforcement learning research is recapitulating the development of classical research in planning and problem solving. After studying the problem of solving "flat" problem spaces, researchers have recently turned their attention to hierarchical methods that incorporate subroutines and state abstractions. This paper gives an overview of the MAXQ value function decomposition and its support for state abstraction and action abstraction.