A course in fuzzy systems and control
A course in fuzzy systems and control
Colearning in Differential Games
Machine Learning
Learning fuzzy rules from iterative execution of games
Fuzzy Sets and Systems - Theme: Modeling and learning
Multi-agent learning for engineers
Artificial Intelligence
An approach to tune fuzzy controllers based on reinforcement learning for autonomous vehicle control
IEEE Transactions on Intelligent Transportation Systems
Adaptive fuzzy control of satellite attitude by reinforcement learning
IEEE Transactions on Fuzzy Systems
Self-learning fuzzy logic controllers for pursuit-evasion differential games
Robotics and Autonomous Systems
| Hi-index | 0.00 |
In this paper we develop a reinforcement fuzzy learning scheme for robots playing a differential game. Differential games are games played in continuous time, with continuous states and actions. Fuzzy controllers are used to approximate the calculation of future reinforcements of the game due to actions taken at a specific time. If an immediate reinforcement reward function is defined, we may use a fuzzy system to tell what is the predicted reinforcement in a specified time ahead. This reinforcement is then used to adapt a fuzzy controller that stores the experience accumulated by the player. Simulations of a modified two car game are provided in order to show the potentiality of the technique. Experiments are performed in order to validate the method. Finally, it should be noted that although the game used as an example involves only two players, the technique may also be used in a multi-game environment.