q-FrozenLake-v1-4x4-noSlippery – AI Model by a1914114315 | AlphaNeural AI

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a1914114315/
q-FrozenLake-v1-4x4-noSlippery

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reinforcement-learning

custom-implementation

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Q-Learning Agent playing FrozenLake-v1-4x4-no_slippery

This repository contains a tabular Q-Learning agent trained on FrozenLake-v1-4x4-no_slippery using Gymnasium.

The agent was implemented as part of the Hugging Face Deep Reinforcement Learning Course.

Evaluation results

Metric	Result
Evaluation episodes	1000
Mean reward	1.0000
Reward standard deviation	0.0000
Success rate	100.00%
Mean episode length	6.00

Agent replay

Q-Learning agent replay

Repository files

q-learning.pkl: Q-table, environment settings and hyperparameters
qtable.npy: NumPy Q-table
results.json: evaluation summary
evaluation_episodes.csv: individual evaluation episodes
training_history.csv: training history, when available
replay.gif: animated agent replay
replay.mp4: video replay, when available

Load the trained agent

python

import pickle
import gymnasium as gym

from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="a1914114315/q-FrozenLake-v1-4x4-noSlippery",
    filename="q-learning.pkl"
)

with open(model_path, "rb") as file:
    model = pickle.load(file)

qtable = model["qtable"]

env = gym.make(
    model["env_id"],
    **model.get("env_kwargs", {})
)

print("Environment:", model["env_id"])
print("Q-table shape:", qtable.shape)
print(
    "Mean reward:",
    model["evaluation"]["mean_reward"]
)

Training configuration

json

{
  "n_training_episodes": 10000,
  "max_steps": 99,
  "learning_rate": 0.7,
  "gamma": 0.95,
  "max_epsilon": 1.0,
  "min_epsilon": 0.05,
  "decay_rate": 0.0005,
  "seed": 42
}