Open Access

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

  • Valdinei Freire da Silva1 and
  • Anna Helena Reali Costa1
Journal of the Brazilian Computer Society15:BF03194507

Received: 7 July 2009

Accepted: 27 August 2009


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, CFQLearning, 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.


machine learning reinforcement learning abstraction partial-policy macro-states