
# **QRDQN** Agent playing **AsteroidsNoFrameskip-v4**
This is a trained model of a **QRDQN** agent playing **AsteroidsNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.

## Usage (with SB3 RL Zoo)

RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib

```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo qrdqn --env AsteroidsNoFrameskip-v4 -orga sb3 -f logs/
python enjoy.py --algo qrdqn --env AsteroidsNoFrameskip-v4  -f logs/
```

## Training (with the RL Zoo)
```
python train.py --algo qrdqn --env AsteroidsNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo qrdqn --env AsteroidsNoFrameskip-v4 -f logs/ -orga sb3
```

## Hyperparameters
```python
OrderedDict([('env_wrapper',
              ['stable_baselines3.common.atari_wrappers.AtariWrapper']),
             ('exploration_fraction', 0.025),
             ('frame_stack', 4),
             ('n_timesteps', 10000000.0),
             ('optimize_memory_usage', True),
             ('policy', 'CnnPolicy'),
             ('normalize', False)])
```

{"library_name": "stable-baselines3", "tags": ["AsteroidsNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "QRDQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "AsteroidsNoFrameskip-v4", "type": "AsteroidsNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "1896.00 +/- 480.65", "name": "mean_reward"}]}]}]}
stable-baselines3 AsteroidsNoFrameskip-v4 deep-reinforcement-learning reinforcement-learning model-index region:us
TAGS
#stable-baselines3 #AsteroidsNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 


# QRDQN Agent playing AsteroidsNoFrameskip-v4
This is a trained model of a QRDQN agent playing AsteroidsNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.

## Usage (with SB3 RL Zoo)

RL Zoo: URL
SB3: URL
SB3 Contrib: URL



## Training (with the RL Zoo)


## Hyperparameters


# QRDQN Agent playing AsteroidsNoFrameskip-v4
This is a trained model of a QRDQN agent playing AsteroidsNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo)

RL Zoo: URL
SB3: URL
SB3 Contrib: URL ## Training (with the RL Zoo) ## Hyperparameters
TAGS
#stable-baselines3 #AsteroidsNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 # QRDQN Agent playing AsteroidsNoFrameskip-v4
This is a trained model of a QRDQN agent playing AsteroidsNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo)

RL Zoo: URL
SB3: URL
SB3 Contrib: URL ## Training (with the RL Zoo) ## Hyperparameters
TAGS
#stable-baselines3 #AsteroidsNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
# QRDQN Agent playing AsteroidsNoFrameskip-v4
This is a trained model of a QRDQN agent playing AsteroidsNoFrameskip-v4
using the stable-baselines3 library
and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)

RL Zoo: URL
SB3: URL
SB3 Contrib: URL## Training (with the RL Zoo)## Hyperparameters