# hps-rl

**Repository Path**: mirrors_esnet/hps-rl

## Basic Information

- **Project Name**: hps-rl
- **Description**: Hyperparameter tuning for deep RL applications
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-03-30
- **Last Updated**: 2026-03-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

*** Copyright Notice and License files have been added ***





# HPS-RL: Hyperparameter tuning for deep RL applications

Genetic Algorithms meets Deep RL for Hyperparameters
Hyperparameter optimization and architecture search can easily become cumbersome
and finding the right hyperparameters can seriously impact the robustness of the deep RL application being developed.

We use genetic algorithms to evolve optimum deep RL architectures in a scalable manner. 

HPS-RL is designed to work with multiple gym enviornments, allow users to test their own optimization functions and tune multi-objective parameters in multiple deep RL algorithms.
HPS-RL uses multi-threading and is being extended with mpipy for distributed processing on HPC.


## Documentation


## Explanations

## Install
Current installation of HPS-RL has been tested on Python 3.6.

## Example 

## Contact Us