Frameworks

  • Support tensorboard tool.
  • Add save and restore APIs in parl.Agent.
  • Add exception traceback in remote module.
  • Disentangle basic classes(e,g., parl.Model) and the computation framework.

Examples

  • Refine benchmark performance of A2C example.
  • Simplify QuickStart example.

Papers

  • Collect some papers relative to model-based reinforcemnt learning topic.

项目简介

A high-performance distributed training framework for Reinforcement Learning

发行版本 5

PARL 1.3

全部发行版

贡献者 23

全部贡献者

开发语言

  • Python 82.6 %
  • C++ 10.3 %
  • JavaScript 3.1 %
  • Shell 1.4 %
  • CMake 1.2 %