Reinforcement Learning Crash Course - How to train our first Reinforcement Learning agent! | Reinforcement Learning Part-3

Looking only at the theoretical portions of Reinforcement Learning, people often get bored. To spice things up, we apply the theory into practice and apply SARSA and Q-Learning to the Cliff Walking Problem. We use a library called OpenAI Gym to spare the time of coding an RL Environment ourselves. Some interesting things were noticed about these algorithms when we compared them. Happy Learning! OpenAI Gym : https://www.gymlibrary.ml/ Gym Github : https://github.com/openai/gym PyCharm : https://www.jetbrains.com/pycharm/download/#section=windows Project Github Link: https://github.com/rajtilakls2510/reinforcement_learning/tree/part_3 Reinforcement Learning Book by Sutton and Barto: http://incompleteideas.net/book/the-book.html Free Reinforcement Learning Course from IIT Madras: https://nptel.ac.in/courses/106106143 Music: bensound.com ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 ⌚Time Stamps⌚ 0:00 - Intro 0:41 - Cliff Walking Problem 2:15 - Gym Interface 4:04 - Gym Cliff Walking Env 5:28 - Setup 7:36 - Coding a Random Agent 15:49 - Coding a SARSA Agent 27:09 - Seeing how the agent performs 33:02 - Coding a Q-Learning Agent 39:58 - Comparing SARSA and Q-Learning 41:11 - Outro