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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.
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.
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.
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.
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.