Reinforcement Learning Demo

This repository contains three reinforcement learning mini-projects, each utilizing a different method of reinforcement learning (RL) and solving distinct OpenAI Gym environments.

Projects Overview

1. Tabular Q-learning

   • Environment: Blackjack
   • Description: Implementing Q-learning with a tabular approach to solve the classic Blackjack game.

2. Deep Q-Network (DQN)

   • Environment: Frozen Lake
   • Description: Using Deep Q-Networks (DQN) to tackle the Frozen Lake environment, a simple grid world where an agent must navigate through frozen terrain to reach a goal.

3. Proximal Policy Optimization (PPO)

   • Environment: Bipedal Walker
   • Description: Applying Proximal Policy Optimization (PPO) to train an agent to control a bipedal walker, balancing and navigating through a challenging environment.