Nofear3344

Nofear3344's Stars

• wangshusen/DRL

  Deep Reinforcement Learning

  3.2k576

• princeton-vl/infinigen

  Infinite Photorealistic Worlds using Procedural Generation

  Language:Python5.3k460

• microsoft/qlib

  Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

  Language:Python15.3k2.6k

• Silvicek/distributional-dqn

  Implementation of 'A Distributional Perspective on Reinforcement Learning' and 'Distributional Reinforcement Learning with Quantile Regression' based on OpenAi DQN baselines.

  Language:Python13125

• sudharsan13296/Deep-Reinforcement-Learning-With-Python

  Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with extensive Math

  Language:Jupyter Notebook339125

• go2sea/C51DQN

  A TensorFlow implementation of DeepMind's A Distributional Perspective on Reinforcement Learning.(C51-DQN)

  Language:Python5618

• Remtasya/Distributional-Multi-Agent-Actor-Critic-Reinforcement-Learning-MADDPG-Tennis-Environment

  The state-of-the-art in multi-agent Reinforcement Learning is the MADDPG algorithm which utilises DDPG actor-critic neural networks where each agent uses centralized critic training but decentralized actor execution, and is capable of learning either cooperative or competitive environments. This is demonstrated on the Unity Tennis Environment.

  Language:Jupyter Notebook2311

• senya-ashukha/quantile-regression-dqn-pytorch

  A short and easy implementation of Quantile Regression DQN | Distributional Reinforcement Learning

  Language:Jupyter Notebook9426

• Kiwoo/distributional_perspective_on_RL

  Implementation of A Distributional Perspective on Reinforcement Learning

  Language:Python3411