# OCARL

**Repository Path**: f-hy/OCARL

## Basic Information

- **Project Name**: OCARL
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-11-14
- **Last Updated**: 2023-11-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Implementation for Object-Category Aware Reinforcement Learning.

We use a modified version of Tianshou and SPACE, which are included in this repository. 
You should install Tianshou first ().

Addional needed packages are listed as follows:

```
pytorch=1.8.1
stable-baselines3=1.1.0
matplotlib=3.0.2

```

We provides the pretrained SPACE model in the 'space_models' dictionary, as well as the dataset to train SPACE in the 'space_datasets'. The config file for SPACE is saved in the 'space_models' dictionary.
To reproduce the results in the 'Evaluation' section, you can just run the following scripts: 

```
python ts_train.py pol_type=ocarl task=hunter env_kwargs.train.spawn_args='Z1C0/Z0C1'
```
The 'pol_type' above can be set to 'ocarl', 'mlp', 'rrl', or 'smorl'; 'task'='hunter' or 'crafter'; env_kwargs.train.spawn_args='Z1C0/Z0C1', 'Z4C0/Z0C4', 'Z1C1' or 'Z4C4'.