From State Compression to State Emphasis: Continuous Aggregation for Policy Gradient Optimization
收藏Mendeley Data2026-05-21 收录
下载链接：
https://data.mendeley.com/datasets/rs2pt2r6rm
下载链接
链接失效反馈官方服务：
资源简介：
This repository implements Hierarchical Proximal Policy Optimization (HPPO) and standard Proximal Policy Optimization (PPO) algorithms using Ray RLlib framework for reinforcement learning experiments.
Requirements
The project requires the following dependencies:
gymnasium==1.1.1
ray==2.10.0
torch==2.0.1
Installation
Install the required packages using pip:
pip install gymnasium==1.1.1 ray==2.10.0 torch==2.0.1
For ALE/Atari environments, you may also need:
pip install ale-py
Project Structure
HPPO-master/
├── main_HPPO.py # HPPO algorithm implementation
├── main_PPO.py # PPO algorithm implementation
├── ray_result_HPPO_all.zip # HPPO training results
├── ray_result_PPO_all.zip # PPO training results
└── README.md # This file
创建时间：
2026-05-09
