how to build bot for atari with openai gym written by alvin wan edited by mark drake reinforcement learning is subfield within control theorywhich concerns controlling systems that change over time and broadly includes applications such as self-driving carsroboticsand bots for games throughout this guideyou will use reinforcement learning to build bot for atari video games this bot is not given access to internal information about the game insteadit' only given access to the game' rendered display and the reward for that displaymeaning that it can only see what human player would see in machine learninga bot is formally known as an agent in the case of this tutorialan agent is "playerin the system that acts according to decision-making functioncalled policy the primary goal is to develop strong agents by arming them with strong policies in other wordsour aim is to develop intelligent bots by arming them with strong decisionmaking capabilities you will begin this tutorial by training basic reinforcement learning agent that takes random actions when playing space invadersthe classic atari arcade gamewhich will serve as your baseline for comparison following thisyou will explore several other techniques -including qlearningdeep -learningand least squares -while building agents that play space invaders and frozen lakea simple game environment included in gym (toolkit released by openai (