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---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 581.50 +/- 104.72
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
```python
from stable_baselines3.common.env_util import make_atari_env
from stable_baselines3.common.vec_env import VecFrameStack
from stable_baselines3 import DQN
from stable_baselines3.common.evaluation import evaluate_policy
from huggingface_sb3 import load_from_hub, package_to_hub
from stable_baselines3.common.utils import set_random_seed
env_id = "SpaceInvadersNoFrameskip-v4"
env = make_atari_env(env_id,
n_envs=12,
# Improving reproducibility
seed=1)
env = VecFrameStack(env, n_stack=4) # Stack last four images
# Improving reproducibility
set_random_seed(42)
# Using these parameters as default: https://huggingface.co/micheljperez/dqn-SpaceInvadersNoFrameskip-v4
model = DQN(policy = "CnnPolicy",
env = env,
batch_size = 32,
buffer_size = 100_000,
exploration_final_eps = 0.01,
exploration_fraction = 0.025,
gradient_steps = 1,
learning_rate = 1e-4,
learning_starts = 100_000,
optimize_memory_usage = True,
replay_buffer_kwargs = {"handle_timeout_termination": False},
target_update_interval = 1000,
train_freq = 4,
# normalize = False,
tensorboard_log = "./tensorboard",
verbose=1
)
f = load_from_hub('masterdezign/dqn2-SpaceInvadersNoFrameskip-v4', 'dqn-SpaceInvadersNoFrameskip-v4.zip')
model = model.load(f)
mean_reward, std_reward = evaluate_policy(model, env)
print(f"Mean reward = {mean_reward:.2f} +/- {std_reward:.2f}")
```
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