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Konzai/
q-FrozenLake-v1

Name: q-FrozenLake-v1
Availability: InStock

FrozenLake-v1-4x4-no_slippery

q-learning

reinforcement-learning

custom-implementation

model-index

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Q-Learning Agent playing FrozenLake-v1

This is a trained model of a Q-Learning agent playing FrozenLake-v1 .

Usage

python

import gymnasium as gym
from huggingface_hub import hf_hub_download
import numpy as np
import pickle

model_file = hf_hub_download(
    repo_id="Konzai/q-FrozenLake-v1",
    filename="q-learning.pkl",
    local_dir="."
)
with open("q-learning.pkl", "rb") as f:
    model = pickle.load(f)
env = gym.make(
    "FrozenLake-v1",
    map_name="4x4",
    is_slippery=False,
    render_mode="human"
)
n_show_episodes = 3
max_steps = model["max_steps"]
q_table = model["q_table"]
for episode in range(n_show_episodes):
    state, info = env.reset()
    for step in range(max_steps):
        action = np.argmax(q_table[state, :])
        state, reward, terminated, truncated, info = env.step(action)
        if terminated or truncated:
            break