  A reinforcement learning agent tries to maximize its cumulative payoff by
interacting in an unknown environment. It is important for the agent to explore
suboptimal actions as well as to pick actions with highest known rewards. Yet,
in sensitive domains, collecting more data with exploration is not always
possible, but it is important to find a policy with a certain performance
guaranty. In this paper, we present a brief survey of methods available in the
literature for balancing exploration-exploitation trade off and computing
robust solutions from fixed samples in reinforcement learning.
