Markov Decision Process (MDP) in Reinforcement Learning
Motivation and Framing Let's consider a real world scenario where a robot is instructed to move forward in a room with two doors, one door is an exit and another door is a fire pit. The robot has to find a path and navigate to the exit. The robot might go to the wrong exit. In short, unlike other algorithms, where the robot knows exactly where it will land if it takes a certain step, the algorithm MDP(Markov Decision Processes) are the framework for the decision making process where the outcome of the game, search, etc are uncertain but the probability of next state is known. A prime example of this problem is Grid world: an AI agent walking through a grid, trying to reach the reward grid. In this kind of problem the movement of the agent might not always go right as commanded i.e. telling the agent to move North makes the agent go North 80% of the time, but 10% times it goes East and 10% times it goes West. This happens because of random behavior mimicking the real world. Formal ...
