  There have been numerous attempts in explaining the general learning
behaviours using model-based and model-free methods. While the model-based
control is flexible yet computationally expensive in planning, the model-free
control is quick but inflexible. The model-based control is therefore immune
from reward devaluation and contingency degradation. Multiple arbitration
schemes have been suggested to achieve the data efficiency and computational
efficiency of model-based and model-free control respectively. In this context,
we propose a quantitative 'value of information' based arbitration between both
the controllers in order to establish a general computational framework for
skill learning. The interacting model-based and model-free reinforcement
learning processes are arbitrated using an uncertainty-based value of
information. We further show that our algorithm performs better than Q-learning
as well as Q-learning with experience replay.
