Learning Algorithms for Small Mobile Robots: Case Study on Maze Exploration
نویسندگان
چکیده
An emergence of intelligent behavior within a simple robotic agent is studied in this paper. Two control mechanisms for an agent are considered — new direction of reinforcement learning called relational reinforcement learning, and a radial basis function neural network trained by evolutionary algorithm. Relational reinforcement learning is a new interdisciplinary approach combining logical programming with traditional reinforcement learning. Radial basis function networks offer wider interpretation possibilities than commonly used multilayer perceptrons. Results are discussed on the maze exploration problem.
منابع مشابه
A Navigation and Obstacle Avoidance Algorithm for Mobile Robots Operating in Unknown, Maze-Type Environments
This paper describes two complementary algorithms developed for mobile robots operating within unknown, maze-type environments. The first is an environmental mapping and navigation algorithm which ensures complete coverage of a maze with apriori unknown wall locations, and the second a stochastic learning automaton approach for general obstacle avoidance within the maze. The environmental mappi...
متن کاملTowards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots
Exploration in an unknown environment is the core functionality for mobile robots. Learning-based exploration methods, including convolutional neural networks, provide excellent strategies without human-designed logic for the feature extraction [1]. But the conventional supervised learning algorithms cost lots of efforts on the labeling work of datasets inevitably. Scenes not included in the tr...
متن کاملMulti-Agent Maze Exploration
Mazes have intrigued the human mind for thousands of years, and have been used to measure mental abilities of laboratory animals. In recent years mazes have been used to measure the artificial intelligence of robots by examining their ability to traverse mazes using maze exploration and solution algorithms. We use a simulation of a multi-agent system and show that it is beneficial to use a grou...
متن کاملDelay Compensation on Fuzzy Trajectory Tracking Control of Omni-Directional Mobile Robots
This paper presents a delay compensator fuzzy control for trajectory tracking of omni-directional mobile robots. Fuzzy logic control (FLC) of the robots is a suitable strategy for dealing with model uncertainties, nonlinearities and disturbances. On the other hand, in many robotic applications such as mobile robots, delay phenomenon is able to substantially deteriorate the behavior of system's...
متن کاملPerformance Comparison of Two Reinforcement Learning Algorithms for Small Mobile Robots
The design of intelligent agents by means of reinforcement learning is studied in this paper. A relational reinforcement learning algorithm is used to achieve a compact knowledge representation. Moreover, this approach allows to improve the learning performance by augmenting the algorithm with the so-called background knowledge. A case study on simulated physical robotic agents is performed and...
متن کامل