  The emerging field of Reinforcement Learning (RL) has led to impressive
results in varied domains like strategy games, robotics, etc. This handout aims
to give a simple introduction to RL from control perspective and discuss three
possible approaches to solve an RL problem: Policy Gradient, Policy Iteration,
and Model-building. Dynamical systems might have discrete action-space like
cartpole where two possible actions are +1 and -1 or continuous action space
like linear Gaussian systems. Our discussion covers both cases.
