  In this work, we present a methodology that enables an agent to make
efficient use of its exploratory actions by autonomously identifying possible
objectives in its environment and learning them in parallel. The identification
of objectives is achieved using an online and unsupervised adaptive clustering
algorithm. The identified objectives are learned (at least partially) in
parallel using Q-learning. Using a simulated agent and environment, it is shown
that the converged or partially converged value function weights resulting from
off-policy learning can be used to accumulate knowledge about multiple
objectives without any additional exploration. We claim that the proposed
approach could be useful in scenarios where the objectives are initially
unknown or in real world scenarios where exploration is typically a time and
energy intensive process. The implications and possible extensions of this work
are also briefly discussed.
