**Model-Based Bayesian Exploration**

## R. Dearden, N. Friedman, and D. Andre

To appear in *Proc. Fifteenth Conf. on Uncertainty in Artificial Intelligence
(UAI)*, 1999.

Postscript version (268K)

PDF version.

### Abstract

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.

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