# nas-rl

**Repository Path**: frontxiang/nas-rl

## Basic Information

- **Project Name**: nas-rl
- **Description**: No description available
- **Primary Language**: Python
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-09-13
- **Last Updated**: 2021-09-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# nas-rl

### Description
Neural Architecture Search poses a problem for Deep Learning. We use a Reinforcement Learning method to solve this issue, with multiple worker agents. The approach uses multiple DDPG agents, which are run by a controller and treats the environment of the dataset as a input to work on. 

- Model: variable number of nodes, fixed number of hidden layers

```
Framework: PyTorch
Mode of Update: Asynchronous
Algorithm: Multi-Agent DDPG
Environment: Datasets
Neural Network: DNNs
Sampling: Replay Buffer
Type of Learning: Actor-Critic Method
```

The controller can be trained on GPUs as well.


### Dependencies
```
torch
numpy
pandas
sklearn
```

### Installation
```
git clone https://github.com/wolflegend99/nas-rl.git
```