Repository containing code and notebooks exploring how to solve Atari's Pong through Reinforcement Learning
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Updated
Oct 21, 2025 - Jupyter Notebook
Repository containing code and notebooks exploring how to solve Atari's Pong through Reinforcement Learning
A collection of Jupyter notebooks implementing core reinforcement learning algorithms: Q-Learning, SARSA, and PPO.
Clean and reproducible implementation of DQN and its extensions (DDQN, Dueling, PER, N-step) for solving CartPole-v1. Includes a modular training pipeline, evaluation script, TensorBoard logging, and experiment notebook.
RL-Odyssey is a research framework for continuous control that implements state-of-the-art RL algorithms (SAC, TD3, PPO, etc.) with clean experiment scripts and interactive notebooks.
Repository containing code and notebooks exploring how to solve Gymnasium's Lunar Lander through Reinforcement Learning
A simple Deep Q-Learning project that trains an agent to land a spacecraft safely in the LunarLander environment using PyTorch and Gymnasium. The notebook shows how a DQN works step-by-step with replay memory, epsilon-greedy exploration, and soft target updates.
This repository hosts Jupyter notebooks showcasing the training of Atari games using a variety of Deep Reinforcement Learning (RL) algorithms such as Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Deep Q-Networks (DQN), Advantage Actor-Critic (A2C), and more.
Repository containing code and notebooks exploring how to solve Gymnasium's Car Racing through Reinforcement Learning
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