
# **Deep Q-Learning based Agent for Atari Breakout**

The agent showcased in this space is trained using the Deep Q-Learning algorithm.
The agent was trained for $$3500$$ episodes with a learning rate of $$0.00001$$ and an epsilon value that decreased linearly over time.

## Usage

```bash
python main.py --model_folder <Name of the folder> --model_name <Name of the model> --save_video 1 --video_name <Name of the video file>
```

{"language": ["en"], "license": "mit", "tags": ["Atari-Breakout-v0", "deep-reinforcement-learning", "reinforcement-learning"], "model-index": [{"name": "Deep Q Learning", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Atari-Breakout-v0", "type": "Atari-Breakout-v0"}, "metrics": [{"type": "mean_reward", "value": 29.0, "name": "mean_reward", "verified": false}]}]}]}
Atari-Breakout-v0 deep-reinforcement-learning reinforcement-learning en license:mit model-index region:us