From Control Theory to Reinforcement Learning: A Unified Tutorial | Events - Concordia University
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A Concordia tutorial bridges classical control theory and modern reinforcement learning, revealing how machines learn to make decisions the same way engineers have always designed them — through feedback and optimization.
Control TheoryReinforcement LearningStochastic OptimizationMulti-Armed Bandit
Theory Briefing
- Concordia's tutorial unifies control theory and reinforcement learning, showing both fields share the same mathematical backbone of feedback and optimization.
- Research interests like decentralized stochastic control and multi-armed bandits reveal how real-world AI decisions mirror classical engineering trade-offs under uncertainty.
- Team theory — coordinating multiple decision-makers — links the tutorial to cutting-edge problems in multi-agent AI and autonomous systems.