This readme serves to give an overview of the flow of the MP.

runner.py - Setup the required parameters and start Simulation.

Simulator - The broad tasks of the Simulator are to train the agent and then test it on a real game. - During the training phase, the agent must learn the optimal parameters to ensure as many bounces as possible. - Training will be done over a certain number of games so you must modular code that allows you to reuse the same code game over game. - For testing, use the parameters learnt on an actualy pong game. Keep playing till the agent loses and track the bounces.

MDP - Sets up the Markov Decision Process that includes states, actions, (refer to the documentation for more details). - Performs actions on states for example, moves the paddle up and the ball in a certain direction. - Keeps track of 2 kinds of states - continuous and discrete.