dgildeh / dql-cartpole-simulator

Training a DQL model with Keras to balance a pole on a cart using OpenAI Gym

Deep Q-Network (DQN) Reinforcement Learning Agent

This project creates and uses a DQN Agent with reinforcement learning to train the agent to balance a stick using the OpenAI Gym Python module.

How It Works

Deep Q-Network (DQN) is the first deep reinforcement learning method proposed by DeepMind.

Essentially the agent tries random actions in an environment (in this case the OpenAI Gym) and based on the state and reaction to that action, will receive either a positive or negative reward, which is used to train the model so it can learn which actions receive the highest rewards based on the state of the current environment:

Through multiple training iterations, the model will eventually learn to balance the stick indefinitely.

Running The Simulation

First install the Python modules required for this project:

pip install -r requirements.txt

Once installed, you can then run the cartpole_train.py script in this repository to start the simulation and training:

python cartpole_train.py

The following command line options are available:

• -h, --help: Display help for command line usage
• -r, --render: Render the CartPole-V1 environment on desktop popup
• -e EPISODES, --episodes EPISODES: Number of training iterations (default=1,000)
• -l LOAD, --load LOAD: Load previously saved model weights from file
• -s SAVE, --save SAVE: Saved model weights to file after training completed

References

A list of useful resources to help you understand how this model works:

• https://keon.io/deep-q-learning/
• https://towardsdatascience.com/welcome-to-deep-reinforcement-learning-part-1-dqn-c3cab4d41b6b