  Reinforcement learning typically assumes that the state update from the
previous actions happens instantaneously, and thus can be used for making
future decisions. However, this may not always be true. When the state update
is not available, the decision taken is partly in the blind since it cannot
rely on the current state information. This paper proposes an approach, where
the delay in the knowledge of the state can be used, and the decisions are made
based on the available information which may not include the current state
information. One approach could be to include the actions after the last-known
state as a part of the state information, however, that leads to an increased
state-space making the problem complex and slower in convergence. The proposed
algorithm gives an alternate approach where the state space is not enlarged, as
compared to the case when there is no delay in the state update. Evaluations on
the basic RL environments further illustrate the improved performance of the
proposed algorithm.
