Welcome to the openai-gym-cartpolev1 repository! This repository contains a reinforcement learning agent that is trained to balance a pole on a cart using the OpenAI Gym environment. The agent is implemented using the Q-learning algorithm, which allows it to learn and improve its performance through trial and error.
To install and run this repository, you will need to have conda installed on your machine.
- Clone this repository to your local machine using
git clone https://github.com/GRawhideMart/openai-gym-cartpolev1.git - Navigate to the repository directory using
cd openai-gym-cartpolev1 - Create a new conda environment using
conda create --name openai-gym-cartpolev1 - Activate the new environment using
conda activate openai-gym-cartpolev1 - Install the required packages using
conda install --file requirements.txt
The agent has a memory which stores the states, actions, and rewards that it experiences as it interacts with the environment. This memory is used to update the agent's Q-values, which represent the expected reward for taking a particular action in a given state. The agent uses these Q-values to decide which action to take in each state, with the goal of maximizing its overall reward.
To run the agent, use the command python agent.py. This will train the agent and display its progress as it learns to balance the pole.
Thank you for visiting this repository! I hope you find it useful in your own exploration of reinforcement learning.