Introduction

In this project, we present 4 convolutional neural networks capable of solving the inverse dynamics problem. These neural networks can be used to predict the delta states of a cartpole trajectory at a time step t, given a sequence of images preceding that time step and an image following the time step. We present several neural network architectures to perform a comparative study of how different hyperparameter choices can affect performance.

Final Report:

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