SB3 PPO. Vectorized 16 env. ~ 9_000_000 timesteps of training. mean_reward=163 +/- 103 . Training for an additional 50_000_000 timesteps resulted in a worse reward when evaluating
28a0b97 metadata
library_name: stable-baselines3
tags:
- Pixelcopter-PLE-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 162.90 +/- 102.90
name: mean_reward
verified: false
ppo Agent playing Pixelcopter-PLE-v0
This is a trained model of a ppo agent playing Pixelcopter-PLE-v0 using the stable-baselines3 library.
Usage (with Stable-baselines3)
TODO: Add your code
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...