PyTorch implementations of algorithms from "Reinforcement Learning: An Introduction by Sutton and Barto", along with various RL research papers.

TraderNet-CRv2 - Combining Deep Reinforcement Learning with Technical Analysis and Trend Monitoring on Cryptocurrency Markets

💡 Grasp - Pick-and-place with a robotic hand 👨🏻‍💻

A Complete Collection of Deep RL Famous Algorithms implemented in Gymnasium most Popular environments

End-to-end RL trading framework with PPO agent, self-attention neural network, custom Gym environment, and advanced backtesting.

LibreGrabbe 16-DOF Robot Hand

Comparative research platform for Deep Reinforcement Learning and heuristic controllers in autonomous racing. Benchmarks DRL (PPO) agents against deterministic baselines in Unity MiniKart, with full reproducibility, human-like evaluation, and performance logs.

This repository implements a Proximal Policy Optimization (PPO) agent that learns to play Super Mario Bros using TensorFlow/Keras and OpenAI Gym. Features CNNs for vision, Actor-Critic architecture, and parallel environments. Train your own Mario master or run a pre-trained one!

Reinforcement learning–based controller for balancing an inverted pendulum using Proximal Policy Optimization (PPO). Supports configurable mass, length, and gravity settings (Earth, lunar, microgravity) with automated training logs, reward visualization, and performance analysis.

This is a project for PPO S&P 500 trading

An RL based model using PPO algorithm leveraging OpenAI Gym environment to play the popular Super Mario game.

How close can LoRA get to full fine-tuning (FullFT) in terms of learning speed, performance, and compute tradeoffs? And under what conditions?

2D orbital rocket sim with PPO in PyTorch. Models thrust, drag, gravity, fuel; agent learns efficient ascent. Includes telemetry & visualization

Stable Baselines3

Developed-an-AWS-DeepRacer-model-using-Python-&-the-PPO-algorithm,-leveraging-TensorFlow-to-train-&-fine-tune-a-deep-reinforcement-learning-model.-Designed-a-custom-reward-function-&-optimized-hyperparameters-to-improve-policy-learning-&-navigation-performance.-Utilized-AWS-infrastructure-for-scalable-training-&-deployment.

This repository explores Reinforcement Learning (RL) through hands-on implementations of key algorithms and environments. It demonstrates how agents learn by interacting with environments, optimizing rewards, and adapting to tasks ranging from Atari games to autonomous driving and custom simulations.

AI-powered production line optimization using reinforcement learning (PPO).

This project is created to demonstrate my beginner level understanding on the concept of Reinforcement Learning and its Algorithms