Technical Note: \cal Q-Learning
Machine Learning
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets: Mathematical Foundations
Rough Sets: Mathematical Foundations
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Eligibility Traces for Off-Policy Policy Evaluation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Collective and Cooperative Group Behaviors: Biologically Inspired Experiments in Robotics
The 4th International Symposium on Experimental Robotics IV
Line-crawling robot navigation: a rough neurocomputing approach
Autonomous robotic systems
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Off-Policy Reinforcement Learning: A Rough Set Approach
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Fundamenta Informaticae - Contagious Creativity - In Honor of the 80th Birthday of Professor Solomon Marcus
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Rough sets and information granulation
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Rough validity, confidence, and coverage of rules in approximation spaces
Transactions on Rough Sets III
Approximation spaces and information granulation
Transactions on Rough Sets III
Calculi of Approximation Spaces
Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
Modelling Complex Patterns by Information Systems
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P 2004)
Towards an Ontology of Approximate Reason
Fundamenta Informaticae - Concurrency Specification and Programming Workshop (CS&P'2001)
Measuring Resemblances Between Swarm Behaviours: A Perceptual Tolerance Near Set Approach
Fundamenta Informaticae - Swarm Intelligence
Rough Set Approach to Behavioral Pattern Identification
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Near Sets. Special Theory about Nearness of Objects
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Approaches to Conflict Dynamics Based on Rough Sets
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Nearness of Objects: Extension of Approximation Space Model
Fundamenta Informaticae - Special Issue on Concurrency Specification and Programming (CS&P)
Behavioral Pattern Identification Through Rough Set Modeling
Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
Calculi of Approximation Spaces
Fundamenta Informaticae - SPECIAL ISSUE ON CONCURRENCY SPECIFICATION AND PROGRAMMING (CS&P 2005) Ruciane-Nide, Poland, 28-30 September 2005
Development of Near Sets Within the Framework of Axiomatic Fuzzy Sets
Fundamenta Informaticae
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This paper introduces a rough set approach to reinforcement learning by swarms of cooperating agents. The problem considered in this paper is how to guide reinforcement learning based on knowledge of acceptable behavior patterns. This is made possible by considering behavior patterns of swarms in the context of approximation spaces. Rough set theory introduced by Zdzisław Pawlak in the early 1980s provides a ground for deriving pattern-based rewards within approximation spaces. Both conventional and approximation space-based forms of reinforcement comparison and the actor-critic method as well as two forms of the off-policy Monte Carlo learning control method are investigated in this article. The study of swarm behavior by collections of biologically-inspired bots is carried out in the context of an artificial ecosystem testbed. This ecosystem has an ethological basis that makes it possible to observe and explain the behavior of biological organisms that carries over into the study of reinforcement learning by interacting robotic devices. The results of ecosystem experiments with six forms of reinforcement learning are given. The contribution of this article is the presentation of several viable alternatives to conventional reinforcement learning methods defined in the context of approximation spaces.