Model Predictive Control and Image Classification

Problem Statement (MPC): In this project, an F1 vehicle is subjected to a Miami race-track consisting of randomly generated obstacles in its course. The defined vehicle is bounded by speed and steering angle constraints, with a goal to complete the track in minimum time and avoid all the random obstacles. The vehicle can detect an obstacle within a range of 250 m. Problem Statement (Image Classification): From the given dataset of 10,000 images comprising of 22 classes, develop an image classification model with an accuracy of more than 45%.

August 1, 2021

Prioritised Experience Replay

In this project, uniform experience replay is applied using a deep Q-network, in which transitions are uniformly sampled from the agents’ replay memory. In the latter part, proportional prioritization is applied to replay the transitions that are categorized as more important, at a higher frequency. Prioritization gives a more robust and effective learning system. Following is the network architecture implemented in PyTorch using novel agent and Q-Table classes. GitHub Repository