This repository explores the application of three reinforcement learning algorithms—Deep Q-Networks (DQN), Double Deep Q-Networks (DDQN), and Proximal Policy Optimization (PPO)—for playing Super Mario Bros using the OpenAI Gym and nes-py emulator. It includes a comparative analysis of these models.

Implementing Double DQN to train it on a custom pong environment in C++ from scratch

📖 Paper: Deep Reinforcement Learning with Double Q-learning 🕹️

Reinforcement learning algorithms in poker games

Jaxplorer is a Jax reinforcement learning (RL) framework for exploring new ideas.

Implementation of the Double Deep Q-Learning algorithm with a prioritized experience replay memory to train an agent to play the minichess variante Gardner Chess

Totally Asymmetric Simple Exclusion Process (TASEP) using reinforcement learning (RL) agents

A complete MataLab laboratory for training and evaluating EMG HGR RL DQN and DDQN models.

Genetic algorithm to select the weights of a MLP to play lunar lander using Reinforcement Learning.

reinforcement learning Double Deep Q Learning (DDQN) method to solve OpenAi Gym "LunarLander-v2" by usnig Double Deep NeuralNetworks

This code is the result of the collaboration of RL Turkey team.

A beginner-friendly repository on Deep Reinforcement Learning (RL), written in PyTorch.

Autonomous Driving W/ Deep Reinforcement Learning in Lane Keeping - DDQN and SAC with kinematics/birdview-images

Deep Reinforcement Learning (PPO) in Autonomous Driving (Carla) [from scratch]

controlling an inverted pendulum using DQN , DDQN and PID controller in gym environment.

Naive implementations of deep reinforcement learning algorithms

⛷ DQN and DDQN algorithms for OpenAI Gym Skiing environment 🎮

Implementation of DQN and DDQN algorithms for Playing Car Racing Game

QWOP agent