Reinforcement Learning in grid-world 1. Created grid world environment through pygame package and optimizing the motion of agent through modified q-learning process. 2. Achieved best possible path for agent by updating its states randomly at each grids/position to reach its final goal. Please checkout github repository for code.Share on Twitter Facebook Google+ LinkedIn Previous Next