https://github.com/yashbhutwala/pong-ai

Deep Q-Learning Networks vs. Policy Gradient Learning in OpenAI Gym's Pong Environment
https://github.com/yashbhutwala/pong-ai

deep-q-learning numpy openai-gym policy-gradient pong python tensorflow

Last synced: 5 days ago
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Deep Q-Learning Networks vs. Policy Gradient Learning in OpenAI Gym's Pong Environment

• Host: GitHub
• URL: https://github.com/yashbhutwala/pong-ai
• Owner: yashbhutwala
• Created: 2017-04-17T16:32:02.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2017-05-02T14:48:04.000Z (almost 8 years ago)
• Last Synced: 2024-12-25T15:42:02.638Z (about 2 months ago)
• Topics: deep-q-learning, numpy, openai-gym, policy-gradient, pong, python, tensorflow
• Language: Python
• Homepage:
• Size: 45.2 MB
• Stars: 0
• Watchers: 3
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Pong AI

This is the final project for CSCI 379 (Intro to AI & CogSci, Spring 2017).

## Team Reveries:

-   Yash Bhutwala

-   Matt McNally

-   Kenny Rader

-   John Simmons

## Problem Statement

Given a set a pixels from a game of Pong, a mechanism for measuring wins and losses,

and a hard-coded opponent agent, could we create an agent that could potentially

beat a human opponent, and what would be the best way to do that?

Our approach tests Deep Q-Learning Networks (DQN) against Policy Gradient (PG) learning in

order to see which algorithm and architecture learns the best.

## How to run the programs:

### Deep Q-Learning (DQN)

Our DQN agent can be ran from the ./dqn directory. You can run it by using the command:

```bash

python main.py --env_name=Pong-v0 --is_train=True --display=True

```

This will run the program on the Pong environment with Training Mode and Rendering

turned on.

### Policy Gradient (PG)

Our PG agent can be ran from the ./pg directory. You can run it by using the command:

`python3 yashPong.py`

This will run the agent in Training Mode and Rendering turned on by default, though

this can be changed in code.

## Acknowledgements:

The code for our DQN approach is modified from existing code from devsisters.

The original repository can be found [here](https://github.com/devsisters/DQN-tensorflow)

Likewise, the code for our PG approach is modified from existing code from Dr. Andrej

Karpathy. The original code can be found [here](https://gist.github.com/karpathy/a4166c7fe253700972fcbc77e4ea32c5)