Dynamic programming: deterministic and stochastic models
Dynamic programming: deterministic and stochastic models
IEEE Transactions on Systems, Man and Cybernetics
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Artificial Intelligence
Proceedings of the seventh international conference (1990) on Machine learning
Self-improving reactive agents: case studies of reinforcement learning frameworks
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Lookahead planning and latent learning in a classifier system
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Learning to Perceive and Act by Trial and Error
Machine Learning
Learning Sequential Decision Rules Using Simulation Models and Competition
Machine Learning - Special issue on genetic algorithms
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Temporal credit assignment in reinforcement learning
Temporal credit assignment in reinforcement learning
Learning in embedded systems
A Real Time Object-Oriented Rational Agent Development System
FAABS '00 Proceedings of the First International Workshop on Formal Approaches to Agent-Based Systems-Revised Papers
An Architectural Framework for Integrated Multiagent Planning, Reacting, and Learning
ATAL '00 Proceedings of the 7th International Workshop on Intelligent Agents VII. Agent Theories Architectures and Languages
2009 Special Issue: Goal-directed control and its antipodes
Neural Networks
Online learning and exploiting relational models in reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An adaptive inventory control for a supply chain
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Q-learning with linear function approximation
COLT'07 Proceedings of the 20th annual conference on Learning theory
Enhanced temporal difference learning using compiled eligibility traces
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Abstraction and generalization in reinforcement learning: a summary and framework
ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
The theory of social functions: challenges for computational social science and multi-agent learning
Cognitive Systems Research
Active learning of relational action models
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
A Tensor Factorization Approach to Generalization in Multi-agent Reinforcement Learning
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
| Hi-index | 0.00 |
Dyna is an AI architecture that integrates learning, planning, and reactive execution. Learning methods are used in Dyna both for compiling planning results and for updating a model of the effects of the agent's actions on the world. Planning is incremental and can use the probabilistic and ofttimes incorrect world models generated by learning processes. Execution is fully reactive in the sense that no planning intervenes between perception and action. Dyna relies on machine learning methods for learning from examples---these are among the basic building blocks making up the architecture---yet is not tied to any particular method. This paper briefly introduces Dyna and discusses its strengths and weaknesses with respect to other architectures.