Efficient reinforcement learning of navigation strategies in an autonomous robot
This paper proposes a reinforcement learning architecture that allows an autonomous robot to acquire efficient navigation strategies in a few trials. Besides fast learning, the architecture has further appealing features. Since it learns from built-in reflexes, the robot is operational from the very beginning. The robot improves its performance incrementally as it interacts with an initially unknown environment, and it ends up learning to avoid collisions even if its sensors cannot detect the obstacles. This is a definite advantage over non-learning reactive robots. The robot also exhibits high tolerance to noisy sensory data and good generalisation abilities. All these features make this learning robot's architecture very well suited to real-world applications. The experimental results obtained are reported, and a real mobile robot in an indoor environment is used to demonstrate the feasibility of this approach.
Bibliographic Reference: Paper presented: IEEE/RSJ International Conference on Intelligent Robots and Systems, München (DE), September 12-16, 1994
Availability: Available from (1) as Paper EN 38513 ORA
Record Number: 199411170 / Last updated on: 1994-11-25
Original language: en
Available languages: en