Agent-Based Autonomous Robotic System Using Deep Reinforcement and Transfer Learning

Real robots have different constraints, such as battery capacity limit, hardware cost, etc., which make it harder to train models and conduct experiments on physical robots. Transfer learning can be used to omit those constraints by training a self-driving system in a simulated environment, with a goal of running it later in a real world. Simulated environment should resemble a real one as much as possible to enhance transfer process. This paper proposes a specification of an autonomous robotic system using agent-based approach. It is modular and consists of various types of components (agents), which vary in functionality and purpose. Thanks to system’s general structure, it may be transferred to other environments with minimal adjustments to agents’ modules. The autonomous robotic system is implemented and trained in simulation and then transferred to real robot and evaluated on a model of a city. A two-wheeled robot uses a single camera to get observations of the environment in which it is operates. Those images are then processed and given as an input to the deep neural network, that predicts appropriate action in the current state. Additionally, the simulator provides a reward for each action, which is used by the reinforcement learning algorithm to optimize weights in the neural network, in order to improve overall performance.