Q-Learning Agent Analysis - Reinforcement Learning p.3

Welcome to part 3 of the Reinforcement Learning series as well as part 3 of the Q learning parts. Up to this point, we've successfully made a Q-learning algorithm that navigates the OpenAI MountainCar environment. The issue now is, we have a lot of parameters here that we might want to tune. Being able to beat the game is one thing, but we might want to beat it quicker, and maybe even try to explore ways to learn faster. In order to do this, we need to start shedding some light onto what exactly we're doing. Text-based tutorial and sample code: https://pythonprogramming.net/q-learning-analysis-reinforcement-learning-python-tutorial/ Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex #reinforcementlearning #machinelearning #python