Control System for Mobile Robot using FPGA-Based Q-Learning Accelerator 🔗Abstract: Q-Learning algorithm is used in smart navigation, path planning and others. The output of the Q-Learning cannot be directly used to drive motors or control robot to specific function in the testing area. To convert Q-Learning action into low level signal, we need a controller that organize the task of the sensors, the actuators and the main accelerator. In this paper, we present the control system for a mobile robot that uses FPGA-Based Q-Learning Accelerator.

ABSTRACT 🔗Design and Implementation of Communication System of Mobile Robot and FPGA for Smart Navigation Problem 🔗By 🔗Mohamad Imam Firdaus 🔗NIM: 13218025 🔗(Undergraduate Program in Electrical Engineering) 🔗Q-Learning is one of the commonly used Reinforcement Learning methods. To accelerate the performance of Q-Learning, a Q-Learning acceleration hardware based on System on Chip was created. The system is implemented on the FPGA development board PYNQ-Z1. To carry out the testing and implementation process in real cases, a Mobile Robot is made that can be integrated with the FPGA development board.

Tutorial LSI Design Contest Okinawa 🔗http://www.lsi-contest.com/2021/shiyou_3-1e.html Reinforcemet Learning 🔗 One of the fields of machine learning. Through trial and error, the agent (the body that performs an action) learns the behavior to be given the maximum reward. Sequence of reinforcement learning 🔗In reinforcement learning, learning is carried out by utilizing four elements of “Environment”, “Agent”, “Action”, and “Rewards”. Environment: Environments with agents Agent: Acting entities Action: The agent’s behavior Rewards: A reward for an action The sequence of reinforcement learning is shown in Fig.