Implementing RF to play the Atari Freeway game

This project has been implemented with the gymnasium Framework: https://gymnasium.farama.org/environments/atari/freeway/

The final selected model was v_20.2

Setup Instructions

Clone the Repository:

git clone https://github.com/JanMuehlnikel/Atari-Freeway-Reinforcement-Learning
cd your-repo

Create and Activate a New Conda Environment:

conda create --name FreewayEnv python=3.10.14
conda activate FreewayEnv

Install pip in the New Conda Environment: @@ -27,39 +27,13 @@ This project has been implemented with the gymnasium Framework: https://gymnasiu
```
pip install -r requirements.txt
```

Results

This project implements a reinforcement learning agent using a Deep Q-Network (DQN). Despite our efforts, the agent was only able to learn the baseline method, which involved simply moving up.

Baseline Method

The baseline method for the agent was to only move up.

Implementation Details

Algorithm: Deep Q-Network (DQN)
Baseline Method: Move up
Reward Structure: The reward might have been too high for moving actions and too low for crashing, affecting the learning process.
Network Complexity: The neural network used might have been too simple to accurately approximate the Q-values, limiting the agent's ability to learn more complex behaviors.
Training Environment: Training was conducted on a CPU with a limited training period, which may have constrained the agent's learning capability.

Possible Improvements

Several factors might have contributed to the agent's limited learning:

Adjusting Rewards: Modifying the reward structure to balance rewards for moving and penalties for crashing.
Network Architecture: Using a more complex neural network to better approximate Q-values.
Training Resources: Utilizing a GPU for training and extending the training period to improve learning efficiency.

These adjustments could potentially enhance the agent's ability to learn beyond the baseline method.

JanMuehlnikel/Atari-Freeway-Reinforcement-Learning