Question D

a.  The reflexive agent will perfom well in the cases where the environments are episodic and discrete. In case of episodic environments, 
the present action of the robot will not affect its future actions. Each episode consists of percieving and acting irrespective of previous 
actions and without concern for future progress. In case of discrete environment, the number of states is fixed and thus for each of the state, 
we can have a best action hardcoded into its table. The agent needs to know the present state and will choose the best/rational action from its table.

b.
   Not deterministic in case when equal adjacent maximum values appear for a square on the path.
c. 
   A randomised agent chooses the aciton randomly from the set of possible actions. Each sequence of actions results into some performance. It is
 always possible that agent chooses the optimal path and result into best performance. However we can always reduce the probability of the robot 
choosing the right path. This can be done by increasing the set of actions possible and a larger subset of "sequence of actions" to be such that 
they perform poorly.  

d. 
   The Rational agent predicts the environment after every constant number of steps. The prediction goes on the hueristic that the dirt distribution 
is semicontinuos (meaning if it decreases, it does so in small amounts though discreetly) so the agent basically is levelling out the effect of noise 
in predicting the values not yet known. As the agent takes its decision based on the expected distribution thus we can expect the robot to perform 
fairly well even in such noisy environment.

e.
The rational agent written in problem C of the problem assumed that the dirt distribution does not change randomly. Moreover it remembers the squares 
it cleaned and tries to avoid visiting the same squares again. So surely though sadly the rational agent designed will completely ignore the arrival 
of dirt on initially cleaned squares. For squares not visited yet, this change does not really matter.