Continuous Proximal Policy Optimization Tutorial with OpenAI gym environment скачать в хорошем качестве

Continuous Proximal Policy Optimization Tutorial with OpenAI gym environment

In this tutorial, we'll learn more about continuous Reinforcement Learning agents and how to teach BipedalWalker-v3 to walk! Reinforcement Learning in the real world is still an ill-defined problem. The agent has to be greedy, but not too greedy... One might conjecture that an optimal agent should have bayesian behavior, which again is not always what we want, nor the design goal of our brain. We want the agent to be curious so they could exploit the environment whenever possible, but not too curious so that they will continue to work for us. If you were the head of a company, it could all be compared to training your employee. You want your employee to be exceptionally efficient at his job, while at the same time you want them to stay working for you. Which is hard, if not impossible. (unless you're Google… of course). For more information watch my tutorial. Text version tutorial: https://pylessons.com/BipedalWalker-v... Full video playlist:    • Introduction to Reinforcement Learning - C...   GitHub code: https://github.com/pythonlessons/Rein... ✅ Support My Channel Through Patreon:   / pylessons   ✅ One-Time Contribution Through PayPal: https://www.paypal.com/paypalme/PyLes...