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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
---
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. |