#计算机科学#StarCraft II Learning Environment
翻译 - 星际争霸II学习环境
#计算机科学#Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning
#计算机科学#StarCraft II - pysc2 Deep Reinforcement Learning Examples
#计算机科学#Modular reinforcement learning library (on PyTorch and JAX) with support for NVIDIA Isaac Gym, Omniverse Isaac Gym and Isaac Lab
#计算机科学#Reaver: Modular Deep Reinforcement Learning Framework. Focused on StarCraft II. Supports Gym, Atari, and MuJoCo.
翻译 - Reaver：模块化深度强化学习框架。专注于《星际争霸2》。支持Gym，Atari和MuJoCo。匹配参考结果。
#计算机科学#Tensorflow implementation of Gated Conditional Pixel Convolutional Neural Network
SkyCode是一个多语言开源编程大模型，采用GPT3模型结构，支持Java, JavaScript, C, C++, Python, Go, shell等多种主流编程语言，并能理解中文注释。模型可以对代码进行补全，拥有强大解题能力，使您从编程中解放出来，专心于解决更重要的问题。| SkyCode is an open source programming model, which adopts...
Spriteworld: a flexible, configurable python-based reinforcement learning environment
#计算机科学#Learning to Communicate with Deep Multi-Agent Reinforcement Learning in PyTorch
NFNets and Adaptive Gradient Clipping for SGD implemented in PyTorch. Find explanation at tourdeml.github.io/blog/
Congratulation to DeepMind! This is a reengineering implementation (on behalf of many other git repo in /support/) of DeepMind's Oct19th publication: [Mastering the Game of Go without Human Knowledge]...
Generative Query Network (GQN) in PyTorch as described in "Neural Scene Representation and Rendering"
#数据仓库#Multi-object image datasets with ground-truth segmentation masks and generative factors.
#计算机科学#An implementation of DeepMind's Relational Recurrent Neural Networks (NeurIPS 2018) in PyTorch.
OpenAI Gym wrapper for the DeepMind Control Suite
Tutorial on how to get started with MuJoCo Simulation Platform. MuJoCo stands for Multi-Joint dynamics with Contact. It was acquired and made freely available by DeepMind in October 2021, and open sou...
#计算机科学#Population Based Training (in PyTorch with sqlite3). Status: Unsupported
#计算机科学#This's an implementation of deepmind Visual Interaction Networks paper using pytorch