  Reinforcement learning systems are often concerned with balancing exploration
of untested actions against exploitation of actions that are known to be good.
The benefit of exploration can be estimated using the classical notion of Value
of Information - the expected improvement in future decision quality arising
from the information acquired by exploration. Estimating this quantity requires
an assessment of the agent's uncertainty about its current value estimates for
states. In this paper we investigate ways of representing and reasoning about
this uncertainty in algorithms where the system attempts to learn a model of
its environment. We explicitly represent uncertainty about the parameters of
the model and build probability distributions over Q-values based on these.
These distributions are used to compute a myopic approximation to the value of
information for each action and hence to select the action that best balances
exploration and exploitation.
