Unified theories of cognition
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Variable Resolution Discretization in Optimal Control
Machine Learning
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Universal Artificial Intelligence: Sequential Decisions Based On Algorithmic Probability
Local learning in probabilistic networks with hidden variables
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
From memory to problem solving: mechanism reuse in a graphical cognitive architecture
AGI'11 Proceedings of the 4th international conference on Artificial general intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Soar-RL: integrating reinforcement learning with Soar
Cognitive Systems Research
Extending mental imagery in sigma
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
Extending mental imagery in sigma
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
Modeling two-player games in the sigma graphical cognitive architecture
AGI'13 Proceedings of the 6th international conference on Artificial General Intelligence
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This article describes the development of reinforcement learning within the Sigma graphical cognitive architecture. Reinforcement learning has been deconstructed in terms of the interactions among more basic mechanisms and knowledge in Sigma, making it a derived capability rather than a de novo mechanism. Basic reinforcement learning --- both model-based and model-free --- are demonstrated, along with the intertwining of model learning.