Training an Autonomous Vehicle using Reinforcement Learning. Implements an optimized Q-Learning driving agent that that navigates a Smartcab through its environment towards a goal. The Smartcab agent simulates transporting passengers from one location to another, evaluating two very important metrics: Safety and Reliability.
Smartcab is a project which utilizes Reinforcement learning techniques to implement a self driving agent in a simplified world. It uses Q-learning algorithm to guide the agent while tackling the environmental constraints.
In this project i have applied reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time.
Content for Udacity's Machine Learning curriculum
Udacity Nanodegree Project 4: Example of reinforcement learning. Training a Smartcab to drive on a rectangular grid.
Applied reinforcement learning to build a simulated vehicle navigation agent. This project involved modelling a complex control problem in terms of limited available inputs, and designing a scheme to automatically learn an optimal driving strategy based on rewards and penalties.
Training a smartcab to drive itself using Reinforcement Learning (Q-Learning)
We apply reinforcement learning techniques for a self-driving agent in a simplified world to aid it in effectively reaching its destinations in the allotted time.
Used Q-learning to train a driving agent to learn an optimal policy in a 2D environment
Project: Train a Smartcab How to Drive using Reinforcement Learning
From just cab, to Smart-Cab
Q-Learning algorithm is implemented of Reinforcement learning to train a smartcab to drive.
Udacity Machine Learning Nanodegree Project
Reinforced Learning
Projects of Udacity's Machine Learning Specialization Nanodegree program.
Applied reinforcement learning to design a scheme to automatically learn an optimal driving strategy based on rewards and penalties.
Applied reinforcement learning to build a simulated vehicle navigation agent. This project involved modeling a complex control problem in terms of limited available inputs, and designing a scheme to automatically learn an optimal driving strategy based on rewards and penalties.
Reinforcement Learning Udacity ML project
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