README.md

---
tags:
- Reinforcement Learning
- PongNoFrameskip-v4
- deep-reinforcement-learning
model-index:
  - name: DQN
    results:
      - task:
          type: reinforcement-learning
          name: reinforcement-learning
        dataset:
          name: PongNoFrameskip-v4
          type: PongNoFrameskip-v4
        metrics:
          - type: mean_reward
            value: '19'
            name: mean_reward
            verified: false
---

# **DQN** Agent Playing **Pong**
This is a trained model of **DQN** agent that plays **PongNoFrameskip-v4**
Pong is a Atari 2600 game imported from Gym environment.
Agent is implemented from Deep Reinforcement Learning by Max Lapan.
The code is present in the github link: [https://github.com/mohit-ix/DeepRL/tree/main/Unit%206]
The performance of agent at different steps is present here: [https://youtu.be/03Pl5Odc2jM]

To use the agent use "03_dqn_play.py" from the github link and type:
```python
python 03_dqn_play.py -m [model_name] -r [recording_location] --no-vis

```

add "-r [recoding_location]" if you want to save the recording
remove "--no-vis" if you want to render the gamplay by the agent.