– Welcome to class
– Action plan
– State transition equations (recap)
– The Truck Backer-Upper
– Vehicle configuration
– Implementation in a Jupyter Notebook
– Manual parking tests
– Training: a two-stage learning process
– State update equations for a trailer truck
– Emulator training strategy
– Training protocol (I)
– Control as RNN (again)
– Training protocol (II)
at , you are describing how the gradient is accumulating backwards through the time steps. This is the same as regular RNN's.. right?
– Unrolling in time (AKA BPTT)
– Successful controller's trajectories
– Additional resources
– PyTorch (partial) implementation
– Bayesian neural nets
– Dropout
– Uncertainty for a regressor (demo)
