saferl-lite 0.1.0

A lightweight, explainable, and constrained reinforcement learning toolkit.

🔐 SafeRL-Lite

A lightweight, explainable, and modular Python library for Constrained Reinforcement Learning (Safe RL) with real-time SHAP & saliency-based explainability, custom metrics, and Gym-compatible wrappers.

---

🌟 Overview

SafeRL-Lite empowers reinforcement learning agents to act under safety constraints, while remaining interpretable and modular for fast experimentation. It wraps standard Gym environments and DQN-based agents with:

• ✅ Safety constraint logic
• 🔍 Visual explainability (SHAP, saliency maps)
• 📊 Violation and reward tracking
• 🧪 Built-in testing and evaluations

---

🔧 Installation

📦 PyPI (coming soon)

pip install saferl-lite

🛠️ From source:

git clone https://github.com/satyamcser/saferl-lite.git
cd saferl-lite
pip install -e .

🚀 Quickstart

Train a constrained DQN agent with saliency-based explainability:

python train.py --env CartPole-v1 --constraint pole_angle --explain shap

🔹 This:

• Adds a pole-angle constraint wrapper to the Gym env

• Logs violations

• Displays SHAP or saliency explanations for agent decisions

🧠 Features

✅ Constrained RL

• Add custom constraints via wrapper or logic class

• Violation logging and reward shaping

• Safe vs unsafe episode tracking

🔍 Explainability

• SaliencyExplainer — gradient-based visual heatmaps

• SHAPExplainer — feature contribution values per decision

• Compatible with any PyTorch-based agent

📊 Metrics

• Constraint violation rate

• Episode reward

• Cumulative safe reward

• Action entropy & temporal behavior stats

📚 Modularity

• Swap out agents, constraints, evaluators, or explainers

• Supports Gym environments

• Configurable training pipeline

📜 Citation

Coming soon after arXiv/preprint release.