Writing good behaviors for mobile robots is a hard task that requires a lot of hand tuning and often fails to consider all the possible configurations that a robot may face. By using reinforcement learning techniques a robot can improve its performance through a direct interaction with the surrounding environment and adapt its behavior in response to some non-stationary events, thus achieving a higher degree of autonomy with respect to pre-programmed robots. In this paper, we propose a novel reinforcement learning approach that addresses the main issues of learning in real-world robotic applications: experience is expensive, explorative actions are risky, control policy must be robust, state space is continuous. Preliminary results performed on a real robot suggest that on-line reinforcement learning, matching some specific solutions, can be effective also in real-world physical environments.
展开▼
