Q-Learning Hexapod: Report
Reinforcement learning techniques prove to be very interesting subjects in both control theory and cognitive sciences. In terms of control theory, building a system perfectly most likely becomes quite difficult, especially when considering sensorimotor errors. By building a system that learns how to accomplish a task on its own, there becomes no need to calculate and predict complex control algorithms. In cognitive sciences, the ability to learn is a core component of cognition. One such simple learning algorithm is the Q-learning algorithm. A six-legged (hexapod) robot will be implemented with a Q-learning algorithm. This project explores the ability of a robotic hexapod agent to learn how to walk, using only the ability to move its legs and tell if it is moving forward. Thus, the hexapod may be seen as an analog for a biological subject lacking all but the basic instincts observed in infants and having no external support or parental figure to learn from. The problem is approached from the perspective of exhaustive experimentation, which is simplified to ensure that the agent learns an acceptable walking gait in a relatively short time. This method results in a hexapod capable of walking forward with some efficiency and continuing to learn as it exploits its own actions. After implementation, the robot learns how to walk.
Read the full Q-Learning Hexapod Project Report.