Displaying 1 to 3 from 3 results

Hands-On-Reinforcement-Learning-With-Python - Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

  •    Jupyter

Reinforcement Learning with Python will help you to master basic reinforcement learning algorithms to the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI and Tensorflow. You will then explore various RL algorithms and concepts such as the Markov Decision Processes, Monte-Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep learning, covering various deep learning algorithms. You will then explore deep reinforcement learning in depth, which is a combination of deep learning and reinforcement learning. You will master various deep reinforcement learning algorithms such as DQN, Double DQN. Dueling DQN, DRQN, A3C, DDPG, TRPO, and PPO. You will also learn about recent advancements in reinforcement learning such as imagination augmented agents, learn from human preference, DQfD, HER and many more.

DeepRL-Tutorials - Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

  •    Jupyter

The intent of these IPython Notebooks are mostly to help me practice and understand the papers I read; thus, I will opt for readability over efficiency in some cases. First the implementation will be uploaded, followed by markup to explain each portion of code. I'll be assigning credit for any code which is borrowed in the Acknowledgements section of this README.





We have large collection of open source products. Follow the tags from Tag Cloud >>


Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.