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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning
| Date | Stars |
|---|---|
| 2026-07-24 | 4726 |
| 2026-07-25 | 4726 |
| 2026-07-28 | 4726 |
| 2026-07-30 | 4726 |
| 2026-07-31 | 4728 |
| 2026-08-06 | 4728 |
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 ## Course in Deep Reinforcement Learning ### Explore the combination of neural network and reinforcement learning. Algorithms and examples in Python & PyTorch Have you heard about the amazing results achieved by [Deepmind with AlphaGo Zero](https://www.youtube.com/watch?time_continue=24&v=tXlM99xPQC8) and by [OpenAI in Dota 2](https://www.youtube.com/watch?v=l92J1UvHf6M)? It's all about deep neural networks and reinforcement learning. Do you want to know more about it? This is the right opportunity for you to finally learn Deep RL and use it on new and exciting projects and applications. Here you'll find an in depth introduction to these algorithms. Among which you'll learn q learning, deep q learning, PPO, actor critic, and implement them using Python and PyTorch. > The ultimate aim is to use these general-purpose technologies and apply them to all sorts of important real world problems. > **Demis Hassabis** This repository contains: <br> <img align="left" src="images/youtube_social_icon_dark.png" alt="drawing" width="64"/> **Lectures (& other content) primarily from DeepMind and Berkley Youtube's Channel.** <br> <img align="left" src="images/GitHub-Mark-64px.png" alt="drawing" width="64"/> **Algorithms (like DQN, A2C, and PPO) implemented in PyTorch and tested on OpenAI Gym: RoboSchool & Atari.** <br> <br> **Stay tuned and follow me on** [](https://twitter.com/andri27_it) and [](https://github.com/andri27-ts) **#60DaysRLChallenge** Now we have also a [**Slack channel**](https://60daysrlchallenge.slack.com/). To get an invitation, email me at [email protected]. Also, email me if you have any idea, suggestion or improvement. To learn Deep Learning, Computer Vision or Natural Language Processing check my **[1-Year-ML-Journey](https://github.com/andri27-ts/1-Year-ML-Journey)** ### Before starting.. Prerequisites * Basic level of Python and PyTorch * [Machine Learning](https://github.com/andri27-ts/1-Year-ML-Journey) * [Basic knowledge in Deep Learning (MLP, CNN and RNN)](https://assoc-redirect.amazon.com/g/r/https://amzn.to/2N3AIlp?tag=andreaaffilia-20) <br> <br> ## Quick Note: my NEW BOOK is out! To learn Reinforcement Learning and Deep RL more in depth, check out my book [**Reinforcement Learning Algorithms with Python**](https://www.amazon.com/Reinforcement-Learning-Algorithms-Python-understand/dp/1789131111)!! <a href="https://www.amazon.com/Reinforcement-Learning-Algorithms-Python-understand/dp/1789131111"> <img src="images/frontcover2.jpg" alt="drawing" width="350" align="right"/> </a> **Table of Contents** 1. The Landscape of Reinforcement Learning 2. Implementing RL Cycle and OpenAI Gym 3. Solving Problems with Dynamic Programming 4. Q learning and SARSA Applications 5. Deep Q-Network 6. Learning Stochastic and DDPG optimization 7. TRPO and PPO implementation 8. DDPG and TD3 Applications 9. Model-Based RL 10. Imitation Learning with the DAgger Algorithm 11. Understanding Black-Box Optimization Algorithms 12. Developing the ESBAS Algorithm 13. Practical Implementation for Resolving RL Challenges <br> <br> <br> ## Index - Reinforcement Learning - [Week 1 - **Introduction**](https://github.com/andri27-ts/60_Days_RL_Challenge#week-1---introduction) - [Week 2 - **RL Basics**](https://github.com/andri27-ts/60_Days_RL_Challenge#week-2---rl-basics-mdp-dynamic-programming-and-model-free-control) - [Week 3 - **Value based algorithms - DQN**](https://github.com/andri27-ts/60_Days_RL_Challenge#week-3---value-function-approximation-and-dqn) - [Week 4 - **Policy gradient algorithms - REINFORCE & A2C**](https://github.com/andri27-ts/60_Days_RL_Challenge#week-4---policy-gradient-methods-and-a2c) - [Week 5 - **Advanced Policy Gradients - PPO**](https://github.com/andri27-t
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:44510a00159b7f06, topic:reinforcement-learning, topic:deep-reinforcement-learning, name:reinforcement learning
