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

Abstract

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.

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Silva, V.F.d., Costa, A.H.R. Compulsory Flow Q-Learning: an RL algorithm for robot navigation based on partial-policy and macro-states. J Braz Comp Soc 15, 65–75 (2009). https://doi.org/10.1007/BF03194507

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Keywords

  • machine learning
  • reinforcement learning
  • abstraction
  • partial-policy
  • macro-states