Metadata-Version: 2.4
Name: rewact_tools
Version: 0.1.4
Summary: RewACT Tools: Plugins and utilities for reward-based learning with LeRobot
Author: Ville Kuosmanen
License: MIT License
        
        Copyright (c) 2025 Ville Kuosmanen
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
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        The above copyright notice and this permission notice shall be included in all
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Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: lerobot>=0.4
Requires-Dist: robocandywrapper
Requires-Dist: numpy
Requires-Dist: scipy>=1.7.0
Requires-Dist: torch
Dynamic: license-file

# rewact_tools

RewACT Tools: Plugins and utilities for reward-based learning with LeRobot

## Installation

```bash
pip install rewact_tools
```

Or install from source:

```bash
cd rewact_tools
pip install -e .
```

## Features

This package provides:

- **Plugins for robocandywrapper**: Reward calculation, advantage computation, and control mode tracking
- **Factory functions**: Utilities for creating pre/post processors for RewACT and ACTvantage policies
- **Dataset utilities**: KeypointReward and LeRobotDatasetWithReward for managing reward labels

## Plugins

- `DenseRewardPlugin`: Adds dense reward calculation with keypoint-based interpolation
- `PiStar0_6CumulativeRewardPlugin`: Computes cumulative rewards for pi*0.6 style training
- `PiStar0_6AdvantagePlugin`: Computes advantages for advantage-conditioned policies
- `ControlModePlugin`: Tracks control mode (human vs autonomous) in datasets

## Usage

```python
from rewact_tools import DenseRewardPlugin, KeypointReward, make_pre_post_processors

# Use plugins with robocandywrapper
# Use factory functions for policy processors
# Use KeypointReward for defining reward keypoints
```



