Using stochastic abstract policies in robotic navigation.

Author(s)

Tiago Matos

• Document type: Master's Dissertation
• Press: São Paulo.
• Institution: Universidade de São Paulo (USP). Escola Politécnica (EP/BC)
• Defense date: 2011-09-06

Examining board members

Anna Helena Reali Costa; Leliane Nunes de Barros; Roseli Aparecida Francelin Romero

• Advisor: Anna Helena Reali Costa

Abstract

Most work in path-planning approaches for mobile robots does not take into account existing solutions to similar problems when learning a policy to solve a new problem, and consequently solves the current navigation problem from scratch, what can be very time consuming. In this work we couple a prior knowledge obtained from a similar solution to a reinforcement learning process. The prior knowledge is represented by an abstract policy. In addition, this work presents a framework for simultaneous reinforcement learning called ASAR, where the abstract policy helps start up the policy for the concrete problem, and both policies are refined through exploration. For the construction of the abstract policy we propose an algorithm called X-TILDE, that builds a stochastic abstract policy, in order to reduce the loss of information. The proposed framework is compared with a default learning algorithm and the results show that it is effective in speeding up policy construction for practical problems. (AU)

• FAPESP's process: 09/04489-9 - RRL-MR: Mobile robot navigation based on relational reinforcement learning
• Grantee: Tiago Matos
• Support Opportunities: Scholarships in Brazil - Master