Supervised fuzzy reinforcement learning for robot navigation

- A novel method for combination of supervised learning and fuzzy reinforcement learning (FRL) is proposed.
- Supervised learning is used for initialization of value (worth) of each candidate action of fuzzy rules in critic-only based FRL algorithms.
- The subsumption architecture is used for robot navigation.
- The proposed algorithm, called SFSL, is used to drive a real robot (E-puck) in an environment with obstacles.
- SFSL outperforms FSL in terms of speed learning, and the number of failures.