Practical overview
When it fits
Use it when learning reinforcement-learning algorithms or looking for a tutorial path from foundational methods to deep and policy-based approaches.
Who it is for
- Learners studying reinforcement-learning methods through algorithm-focused tutorials.
- Python developers comparing approaches such as DQN, policy gradients, actor-critic and PPO.
- Readers exploring examples associated with environments such as BipedalWalker and LunarLander.
Documented uses
- Study foundational methods including Q-learning, Sarsa and Sarsa(lambda).
- Compare DQN variants, including Double DQN, prioritized experience replay and Dueling DQN.
- Explore policy-gradient, actor-critic, DDPG, A3C and PPO tutorials.
- Browse experiment examples involving a 2D car, robot arm, BipedalWalker and LunarLander.
Requirements & limits
The repository is public and lists the MIT license. Its README links to an English-language video playlist and Chinese-language learning material.
- The contents span both basic methods and advanced algorithms, so readers can select topics by method.
- The README links tutorial folders directly and lists several environment-based experiments.
- The repository title identifies TensorFlow, but the available documentation does not specify runtime requirements.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Choose a reinforcement-learning tutorial topic
Use the repository’s tutorial index to select a documented reinforcement-learning method to study.
Before you start
- Access to the public repository README.
Steps
Open the repository README.
Review its Tutorials list, which includes introductory methods such as Q-learning and Sarsa, and advanced topics such as DQN, DDPG, A3C, and PPO.
Choose a listed topic and follow its linked tutorial directory.
Expected outcome
A tutorial topic and its corresponding repository directory are identified for study.
Guide limitations
- The README index does not document setup, runtime requirements, or execution steps for the individual tutorials.
- The README does not establish that the listed tutorials run successfully in a particular environment.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 15 October 2026
- Original website title
- MorvanZhou/Reinforcement-learning-with-tensorflow · Official homepage title · retained in its source language