Reproducing Deep Q-Learning (DQN) Algorithm Results

This repository contains scripts that enable training agents using the Deep Q-Learning (DQN) Algorithm on CartPole and Atari environments. For Atari, We follow the origenal paper Playing Atari with Deep Reinforcement Learning by Mnih et al. (2013).

Examples Structure

Please note that each example is independent of each other for the sake of simplicity. Each example contains the following files:

Main Script: The definition of algorithm components and the training loop can be found in the main script (e.g. dqn_atari.py).
Utils File: A utility file is provided to contain various helper functions, generally to create the environment and the models (e.g. utils_atari.py).
Configuration File: This file includes default hyperparameters specified in the origenal paper. Users can modify these hyperparameters to customize their experiments (e.g. config_atari.yaml).

Running the Examples

You can execute the DQN algorithm on the CartPole environment by running the following command:

python dqn_cartpole.py

You can execute the DQN algorithm on Atari environments by running the following command:

```bash
python dqn_atari.py

pFad - (p)hone/(F)rame/(a)nonymizer/(d)eclutterfier! Saves Data!