This Repository contains a series of google colab notebooks which I created to help people dive into deep reinforcement learning.This notebooks contain both theory and implementation of different algorithms.

Implementing Deep Reinforcement Learning Algorithms in Python for use in the MuJoCo Physics Simulator

Rocket Trajectory Optimization Using REINFORCE Algorithm

A from-scratch implementation of the REINFORCE policy gradient algorithm in PyTorch on CartPole-v1, featuring baseline subtraction and hyperparameter ablation experiments.

This repo implements the REINFORCE algorithm for solving the Cart Pole V1 environment of the Gymnasium library using Python 3.8 and PyTorch 2.0.1.

Policy and value analysis using gymnasium's lunar lander-v3 simulation environment

The objective of this project is to develop an autonomous agent to perform well in the first person shooting games using various reinforcement learning techniques.

Hands-on implementations of Reinforcement Learning algorithms from scratch, progressing from classical methods to deep RL with practical experiments.

On-policy RL (VPG, PPO) algorithms from scratch

Own researches in reinforcement learning using openai-gym.