Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use andyv237/SpaceInvadersNoFrameskip-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use andyv237/SpaceInvadersNoFrameskip-v4 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="andyv237/SpaceInvadersNoFrameskip-v4", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
| !!python/object/apply:collections.OrderedDict | |
| - - - batch_size | |
| - 1024 | |
| - - buffer_size | |
| - 100000 | |
| - - env_wrapper | |
| - - stable_baselines3.common.atari_wrappers.AtariWrapper | |
| - - exploration_final_eps | |
| - 0.05 | |
| - - exploration_fraction | |
| - 0.3 | |
| - - frame_stack | |
| - 4 | |
| - - gradient_steps | |
| - 8 | |
| - - learning_rate | |
| - 0.0001 | |
| - - learning_starts | |
| - 20000 | |
| - - n_envs | |
| - 32 | |
| - - n_timesteps | |
| - 1000000.0 | |
| - - optimize_memory_usage | |
| - false | |
| - - policy | |
| - CnnPolicy | |
| - - target_update_interval | |
| - 1000 | |
| - - train_freq | |
| - 4 | |