Reinforcement Learning
stable-baselines3
BipedalWalkerHardcore-v3
deep-reinforcement-learning
Eval Results (legacy)
Instructions to use SimingSiming/ppo-BipedalWalkerHardcore-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use SimingSiming/ppo-BipedalWalkerHardcore-v3 with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="SimingSiming/ppo-BipedalWalkerHardcore-v3", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
File size: 659 Bytes
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library_name: stable-baselines3
tags:
- BipedalWalkerHardcore-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 7.09 +/- 2.73
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BipedalWalkerHardcore-v3
type: BipedalWalkerHardcore-v3
---
# parameters <br>
model = A2C(policy = "MlpPolicy", <br>
env = env, <br>
n_steps = 256, <br>
learning_rate = 0.001, <br>
gamma = 0.99, <br>
verbose=1) <br> |