Technical Note: \cal Q-Learning
Machine Learning
TD(λ) Converges with Probability 1
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Ant Colony Optimization
Using Distributed-Shared Memory Mechanisms for Agents Communication in a Distributed System
ISDA '07 Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications
Agent-based protein structure prediction
Multiagent and Grid Systems - Multi-agent systems for medicine, computational biology, and bioinformatics
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Combinatorial optimization is the seeking for one or more optimal solutions in a well defined discrete problem space. The optimization methods are of great importance in practice, particularly in the engineering design process, the scientific experiments and the business decision-making. We are investigating in this paper a distributed reinforcement learning based approach for solving combinatorial optimization problems. We are particularly focusing on the bidimensional protein folding problem, an NP-complete problem that refers to predicting the bidimensional structure of a protein from its amino acid sequence, an important optimization problem within many fields including bioinformatics, biochemistry, molecular biology and medicine. Our model is based on a distributed Q-learning approach. The experimental evaluation of the proposed system has provided encouraging results, indicating the potential of our proposal.