
A(n) **ASAC** model trained on the **atari_freeway** environment.

This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/


## Downloading the model

After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r MattStammers/asac-atari_freeway
```

    
## Using the model

To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m sf_examples.atari.enjoy_atari --algo=ASAC --env=atari_freeway --train_dir=./train_dir --experiment=asac-atari_freeway
```


You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
    
## Training with this model

To continue training with this model, use the `train` script corresponding to this environment:
```
python -m sf_examples.atari.train_atari --algo=ASAC --env=atari_freeway --train_dir=./train_dir --experiment=asac-atari_freeway --restart_behavior=resume --train_for_env_steps=10000000000
```

Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.

## SOTA Performance

This model is nearing SOTA performance for the Freeway environment: https://www.endtoend.ai/envs/gym/atari/freeway/ beating TQC and certainly DQN/PPO who both failed to converge after 10 million timesteps. 

The composite score at 10 million timesteps is ~32 which is only two points off SOTA of 34. It appears that with PPO even after 2BN timesteps performance can only reach 33.6 - https://huggingface.co/edbeeching/atari_2B_atari_freeway_3333

I suspect that as with QR-DQN the SAC and TQC models can reach 34 - they just need more training to do so. 
{"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "ASAC", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "atari_freeway", "type": "atari_freeway"}, "metrics": [{"type": "mean_reward", "value": "32.40 +/- 0.49", "name": "mean_reward", "verified": false}]}]}]}
sample-factory tensorboard deep-reinforcement-learning reinforcement-learning model-index region:us