Compulsory Flow Q-Learning: an RL algorithm for robot navigation based on partial-policy and macro-states

reinforcement learning is carried out on-line, through trial-and-error interactions of the agent with the environment, which can be very time consuming when considering robots. in this paper we contribute a new learning algorithm, cfq-learning, which uses macro-states, a low-resolution discretisation of the state space, and a partial-policy to get around obstacles, both of them based on the complexity of the environment structure. the use of macro-states avoids convergence of algorithms, but can accelerate the learning process. in the other hand, partial-policies can guarantee that an agent fulfils its task, even through macro-state. experiments show that the cfq-learning performs a good balance between policy quality and learning rate.