Reinforcement Learning
stable-baselines3
SpaceInvadersNoFrameskip-v4
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use Preethi0205/dqn-SpaceInvadersNoFrameskip-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Preethi0205/dqn-SpaceInvadersNoFrameskip-v4 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Preethi0205/dqn-SpaceInvadersNoFrameskip-v4", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
DQN Agent playing SpaceInvadersNoFrameskip-v4
This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 with Atari wrappers and CnnPolicy for Unit 3 of the Hugging Face Deep Reinforcement Learning Course.
Evaluation Results
- Mean Reward: 620.00 +/- 145.50
- Threshold Required: >= 200
- Pass Status: Passed ✅
Hyperparameters
buffer_size: 100000
learning_rate: 0.0001
batch_size: 32
learning_starts: 10000
target_update_interval: 1000
train_freq: 4
gradient_steps: 1
exploration_fraction: 0.1
exploration_final_eps: 0.01
- Downloads last month
- 6
Evaluation results
- mean_reward on SpaceInvadersNoFrameskip-v4self-reported620.00 +/- 145.50