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pavankumargowda/
q-FrozenLake-v1-4x4-Slippery-MC-Optimized

Name: q-FrozenLake-v1-4x4-Slippery-MC-Optimized
Availability: InStock

FrozenLake-v1-4x4-slippery

monte-carlo

reinforcement-learning

custom-implementation

model-index

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Monte Carlo Policy Iteration Agent playing FrozenLake-v1 (Slippery)

This is a trained model of a Monte Carlo Policy Iteration agent playing FrozenLake-v1 with slippery surfaces.

Usage

python

from huggingface_hub import hf_hub_download
import pickle
import gym

repo_id = "pavankumargowda/q-FrozenLake-v1-4x4-Slippery-MC-Optimized"
filename = "q-learning.pkl"
pickle_model = hf_hub_download(repo_id=repo_id, filename=filename)
with open(pickle_model, 'rb') as f:
    model = pickle.load(f)
env = gym.make(model["env_id"], map_name="4x4", is_slippery=True)