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---
tags:
- SpaceInvadersNoFrameskip-v4
- deep-rl-course
- dqn
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 340.50 +/- 45.20
name: mean_reward
verified: false
---
# DQN Agent playing SpaceInvadersNoFrameskip-v4 👾
This is a trained DQN agent playing SpaceInvadersNoFrameskip-v4.
**This model was trained as part of the Hugging Face Deep RL Course Unit 3.**
## Training Details
- **Algorithm**: Deep Q-Network (DQN)
- **Environment**: SpaceInvadersNoFrameskip-v4 (Atari)
- **Library**: Stable Baselines3
- **Training timesteps**: 1,000,000
- **Evaluation**: 340.50 +/- 45.20 (10 episodes)
## Usage
```python
from stable_baselines3 import DQN
from stable_baselines3.common.env_util import make_atari_env
from stable_baselines3.common.vec_env import VecFrameStack
# Create environment
env = make_atari_env('SpaceInvadersNoFrameskip-v4', n_envs=1)
env = VecFrameStack(env, n_stack=4)
# Load the model
model = DQN.load("dqn-SpaceInvadersNoFrameskip-v4", env=env)
# Enjoy trained agent
obs = env.reset()
for i in range(1000):
action, _states = model.predict(obs, deterministic=True)
obs, rewards, dones, info = env.step(action)
env.render()
```
This model achieves good performance on the SpaceInvaders Atari game, scoring well above the target score of 200 for the Deep RL Course Unit 3.
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