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q-FrozenLake-v1-4x4-noSlippery
berkde/q-FrozenLake-v1-4x4-noSlippery
q-FrozenLake-v1-4x4-noSlippery is a reinforcement learning model from berkde. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product.
This is a trained model of a Q-Learning agent playing FrozenLake-v1-8x8-noSlippery.
Access
Public
Updated Nov 17, 2025
Repo size
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.mp439.2 KB · 91%
At a glance
- Task
- Reinforcement Learning
- Access
- Public
- Created
- Nov 17, 2025
- Updated
- Nov 17, 2025
- SHA
- 0039afde
Model card
From the Hugging Face model README
Q-Learning Agent playing FrozenLake-v1-8x8-noSlippery
This is a trained model of a Q-Learning agent playing FrozenLake-v1-8x8-noSlippery.
Usage
from huggingface_hub import hf_hub_download
import pickle, gymnasium as gym, numpy as np
# Download Q-table
pkl_path = hf_hub_download(repo_id="berkde/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
with open(pkl_path, "rb") as f:
model = pickle.load(f)
env = gym.make(
model["env_id"],
map_name=model.get("map_name"),
is_slippery=model.get("slippery", True),
render_mode="human",
)
state, _ = env.reset()
for _ in range(model["max_steps"]):
action = np.argmax(model["qtable"][state])
state, reward, terminated, truncated, _ = env.step(action)
if terminated or truncated:
break
- Task
- Reinforcement Learning
- Created
- Nov 17, 2025
- Updated
- Nov 17, 2025