Reinforcement Learning
sample-factory
doom_health_gathering_supreme
vizdoom
ppo
deep-reinforcement-learning
deep-rl-course
Eval Results (legacy)
Instructions to use Akshaykumar4321/rl_course_vizdoom_health_gathering_supreme with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sample-factory
How to use Akshaykumar4321/rl_course_vizdoom_health_gathering_supreme with sample-factory:
python -m sample_factory.huggingface.load_from_hub -r Akshaykumar4321/rl_course_vizdoom_health_gathering_supreme -d ./train_dir
- Notebooks
- Google Colab
- Kaggle
Sample-Factory PPO Agent Playing doom_health_gathering_supreme
This is a trained model of a Sample-Factory PPO agent playing doom_health_gathering_supreme.
Evaluation Results
- Environment:
doom_health_gathering_supreme - Mean Reward:
6.85 +/- 1.15 - Target Threshold:
>= 5.0(Certified Pass)
Replay Video
Below is a demonstration of the trained agent navigating the 3D ViZDoom environment to collect health packs:
Training Configuration
{
"algo": "PPO",
"env": "doom_health_gathering_supreme",
"experiment": "doom_health_gathering_supreme",
"train_for_env_steps": 4000000,
"num_workers": 8,
"num_envs_per_worker": 4,
"batch_size": 2048,
"gamma": 0.99,
"gae_lambda": 0.95,
"learning_rate": 0.0001,
"encoder_custom": "make_vizdoom_encoder",
"use_rnn": true,
"rnn_type": "gru",
"rnn_num_layers": 1
}
- Downloads last month
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Evaluation results
- mean_reward on doom_health_gathering_supremeself-reported6.85 +/- 1.15