Learning To Play Soccer using Imitative Reinforcement
- Author: Sven Behnke and Maren Bennewitz
- In Proceedings of ICRA 2005
Workshop on Social Aspects of Robot Programming through Demonstration,
Barcelona, Spain, April 2005.
- Abstract:
The reinforcement framework is a principled approach for agents
learning to act in an environment.In the long run, reinforcement
learning finds optimal policies. However, a physical agent, such as a
humanoid robot, acting in the real world can perform only a limited
number of trails, and consequently has only access to limited
experience. With such limitations, the exhaustive exploration of
high-dimensional state and action spaces is not feasible. One approach
to this dilemma is to utilize experiences of other agents by imitating
their behavior. If the agents are sufficiently similar, this can
speed-up learning dramatically.
We propose to give the learning agent access to the Q-values of an
experienced agent. The learner combines them with its own Q-values in
order to determine its policy. This should head-start learning.We plan
to evaluate the effects of this knowledge transfer in a task derived
from the RoboCup soccer domain using a humanoid robot.
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to selected robotic soccer publications