An Example for Reinforcement Learning using Q-learning with epsilon-greedy exploration(The deterministic cleaning-robot MDP a cleaning robot has to collect a used can also has to recharge its batteries. the state describes the position of the robot and the action describes the direction of motion. The robot can move to the left or to the right. The first (1) and the final (6) states are the terminal states. The goal is to find an optimal policy that maximizes the return from any initial state. Here the Q-learning epsilon-greedy exploration algorithm (in Reinforcement learning) is used.)