This course discusses nonlinear dynamics and control of underactuated mechanical systems, with an emphasis on machine learning methods.

Lecture 22: Actor-Critic methods

Lecture 23: Case studies in computational underactuated control

Lecture 21: Policy improvement

Lecture 20: Temporal difference learning with function approximation

Lecture 19: Temporal difference learning

Lecture 12: Walking (continued)

Lecture 18: Stochastic Gradient Descent 2

Lecture 16: Introducing stochastic optimal control

Lecture 17: Stochastic Gradient Descent

Lecture 10: Trajectory stabilization and iterative linear quadratic regulator (iLQR)

Lecture 11: Walking

Lecture 8: Dynamic programming (DP) and policy search

Lecture 9: Trajectory optimization

Lecture 15: Global policies from local policies

Lecture 14: Feasible motion planning

Lecture 13: Running

Lecture 4: Optimal control of the double integrator (continued)

Lecture 7: Swing-up control of acrobot and cart-pole systems

Lecture 5: Numerical optimal control (dynamic programming)

Lecture 3: Optimal control of the double integrator

Thats awsooom

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