Autonomous lane keeping based on approximate Q-learning

Obstacle avoidance is one of the most important problems in autonomous robots. This paper suggests a collision avoidance system using reinforcement learning. Hand-crafted features are used to approximate Q value. With off-line learning, we develop a general collision avoidance system and use this system to unknown environment. Simulation results show that our mobile robot agent using reinforcement learning can safely explore a corridor even if the agent does not know the shape of corridor at all.