Q-learning using fuzzified states and weighted actions and its application to omni-direnctional mobile robot control

The conventional Q-learning algorithm is described by a finite number of discretized states and discretized actions. When the system is represented in continuous domain, this may cause an abrupt transition of action as the state rapidly changes. To avoid this abrupt transition of action, the learning system requires fine-tuned states. However, the learning time significantly increases and the system becomes computationally expensive as the number of states increases. To solve this problem, this paper proposes a novel Q-learning algorithm, which uses fuzzified states and weighted actions to update its state-action value. By applying the concept of fuzzy set to the states of Q-learning and using the weighted actions, the agent efficiently responds to the rapid changes of the states. The proposed algorithm is applied to omni-directional mobile robot and the results demonstrate the effectiveness of the proposed approach.