Concept

State space (computer science)

Related lectures (32)

Coupling of Markov Chains: Ergodic Theorem

Explores the coupling of Markov chains and the proof of the ergodic theorem, emphasizing distribution convergence and chain properties.

Reinforcement Learning for Pacman

Covers the application of reinforcement learning to teach Pacman to play autonomously by trial and error.

StateSpace ControlDesign

Explores full state feedback control design, focusing on pole placement and linear state-feedback controller design for systems like a pendulum.

Symbolic Representation of State Spaces

Delves into symbolic representation of state spaces using decision diagrams for high-level Petri nets, showcasing efficient encoding techniques and benchmark results.

Multivariable Control: System Theory and Linear Systems

Introduces multivariable control, covering system theory, linear systems, and time discretization.

State-Space Representation: Structure Theorem

Covers the structure theorem for state-space representations and companion forms.

Markov Chains: Reversibility & Convergence

Covers Markov chains, focusing on reversibility, convergence, ergodicity, and applications.

Balanced Realization: SISO Case

Covers the concept of balanced realization in the SISO case, focusing on system observability and controllability.

Controllability and Reachability

Explores reachability and controllability in multivariable control systems, discussing tests, proofs, and their implications.

Dynamic Games: Backward Induction and Nash Equilibria

Covers dynamic games, focusing on backward induction and finding Nash equilibria in two-player scenarios.

Kalman Filter: State Space Models

Introduces the Kalman Filter for estimating the state of a dynamic system from noisy measurements, covering prediction, updating, and filtering steps.

Reinforcement Learning: Q-Learning

Introduces Q-Learning, Deep Q-Learning, REINFORCE algorithm, and Monte-Carlo Tree Search in reinforcement learning, culminating in AlphaGo Zero.