Metadata-Version: 2.4
Name: pb_deepRL
Version: 0.1.dev27
Summary: All the utils/helper functions realted to Deep RL is in this package.
Author-email: Pranav Bhatia <pranavbh184@gmail.com>
Project-URL: Homepage, https://github.com/pranavb1234/rl
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: torch>=2.4.0
Requires-Dist: gymnasium[classic-control,other]<1.3.0,>=0.29.1
Provides-Extra: tests
Requires-Dist: pytest; extra == "tests"
Requires-Dist: pytest-cov; extra == "tests"
Requires-Dist: mypy; extra == "tests"
Requires-Dist: ruff; extra == "tests"
Requires-Dist: black; extra == "tests"

# Reinforcement-Learning
Fail Fast, Learn Faster


This repository contains the implementation and notes of different Reinforcement Learning Algorithms and techniques I have learned throughout time. 

Each algorithm uses GYM environments [gym environements](https://gymnasium.farama.org/environments/).

## Algorithms

1. [Model based learning - Dynamic Programming](./model-based-learning/)
   * [Policy Iteration](./model-based-learning/policy_iteration.ipynb/)
   * [Value Iteration](./model-based-learning/value_iteration.ipynb/)
2. [Model free learning](./model-free-learning/)
   * [Monte Carlo](./model-free-learning/monte_carlo)
      * [Monte Carlo](./model-free-learning/monte_carlo/Monte_Carlo.ipynb)
      * [Online Monte Carlo](./model-free-learning/monte_carlo/online_monte_carlo.ipynb)
      * [Monte Carlo with decay scheduler](./model-free-learning/monte_carlo/mc_with_decay_scheduler.ipynb)
   * [SARSA](./model-free-learning/Sarsa.ipynb)
   * [Q-learning](./model-free-learning/Q_learning.ipynb)
   * [TD_N](./model-free-learning/TD_N.ipynb)
3. [Deep Reinforcement Learning](./deep-rl/)
   * [Value Based](./deep-rl/value-based/)
      * [Deep Q learning](./deep-rl/value-based/Deep_Q_learning.ipynb)
      
