End-to-end Lidar-Driven Reinforcement Learning for Autonomous Racing
Meraj Mammadov
Reinforcement Learning (RL) has emerged as a transformative approach in the
domains of automation and robotics, offering powerful solutions to complex
problems that conventional methods struggle to address. In scenarios where the
problem definitions are elusive and challenging to quantify, learning-based
solutions such as RL become particularly valuable. One instance of such
complexity can be found in the realm of car racing, a dynamic and unpredictable
environment that demands sophisticated decision-making algorithms. This study
focuses on developing and training an RL agent to navigate a racing environment
solely using feedforward raw lidar and velocity data in a simulated context.
The agent's performance, trained in the simulation environment, is then
experimentally evaluated in a real-world racing scenario. This exploration
underlines the feasibility and potential benefits of RL algorithm enhancing
autonomous racing performance, especially in the environments where prior map
information is not available.