Metadata-Version: 2.4
Name: rl-labs
Version: 1.0.0
Summary: Comprehensive Reinforcement Learning library containing implementations from Labs 1-7
Author: Sameer Rizwan
Author-email: "Sameer Rizwan (XIE)" <xie19113@gmail.com>
License: MIT
Keywords: reinforcement-learning,machine-learning,ai,gymnasium,frozenlake,monte-carlo,temporal-difference
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Education
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: gymnasium>=0.29.1
Requires-Dist: numpy>=1.21.0
Requires-Dist: matplotlib>=3.5.0
Provides-Extra: dev
Requires-Dist: pytest>=6.0; extra == "dev"
Requires-Dist: black; extra == "dev"
Requires-Dist: flake8; extra == "dev"
Requires-Dist: twine; extra == "dev"
Requires-Dist: build; extra == "dev"
Provides-Extra: full
Requires-Dist: pygame>=2.5.2; extra == "full"
Dynamic: author
Dynamic: license-file
Dynamic: requires-python

# RL Labs Library

A comprehensive Python library containing reinforcement learning implementations from Labs 1-7. This library provides educational implementations of fundamental RL algorithms using Gymnasium environments.

## Features

- **Lab 1**: Basic agent-environment interaction with FrozenLake
- **Lab 2**: GridWorld MDP implementation with transition probabilities
- **Lab 3**: Markov Reward Processes and Monte Carlo estimation
- **Lab 4**: Policy evaluation and value iteration algorithms
- **Lab 5**: Policy improvement and policy iteration methods
- **Lab 6**: Value iteration with convergence analysis
- **Lab 7**: Temporal Difference learning (MC, TD(0), TD(λ))

## Installation

```bash
pip install rl-labs
