hasnainroopawalla / Deep-Q-Learning

Playing Atari Games (OpenAI Gym) using Deep Reinforcement Learning.

Deep Q-Learning (Reinforcement Learning)

This Python Package can be used to train an agent to play various Atari Games (OpenAI Gym) using Deep Q-Learning.

📝 Table of Contents

• Getting Started
• Agents

🏁 Getting Started

Install all dependencies:

$ pip install -r requirements.txt

Basic usage:

$ python -m dqn --agent <agent_name> --mode <train/simulate>

Parameters

• --agent: Specify the agent to be used (Refer to Agents).
• --mode: Train an agent (train) or simulate a trained agent (simulate).

Agents

CartPole-v0 (--agent cartpole)

More information here.

The following simulation is 3 episodes of a trained CartPole agent receiving a mean reward of 200.0 after training for 1000 episodes.

To Train:

$ python -m dqn --agent cartpole --mode train

To Simulate:

$ python -m dqn --agent cartpole --mode simulate

Pong-V0 (--agent pong)

In-progress