
# **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}")
```

{"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "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": "581.50 +/- 104.72", "name": "mean_reward"}]}]}]}
stable-baselines3 SpaceInvadersNoFrameskip-v4 deep-reinforcement-learning reinforcement-learning model-index region:us
TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 


# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library.

## Usage (with Stable-baselines3)



# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library. ## Usage (with Stable-baselines3)
TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 # DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library. ## Usage (with Stable-baselines3)
passage: TAGS
#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
# DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4
using the stable-baselines3 library.## Usage (with Stable-baselines3)