QLearning-FrozenLake-v1-4x4-NoSlippery

🧠 Q-Learning - FrozenLake-v1 (4x4 NoSlippery)

Reinforcement Learning Implementation using Q-Learning on FrozenLake-v1 (4x4 NoSlippery Environment)
Trained agent learns to navigate the frozen lake without slipping — reaching the goal optimally using value-based Q-Learning.

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🚀 Overview

This project implements Q-Learning, one of the most fundamental Value-Based Reinforcement Learning algorithms, on Gymnasium’s FrozenLake-v1 environment with the 4x4 grid and no slipperiness enabled.

The environment is deterministic, allowing the Q-Learning agent to converge faster and find an optimal policy through iterative updates of the Q-table.

Environment: FrozenLake-v1
Mode: 4x4, is_slippery=False
Algorithm: Q-Learning
Framework: Python + NumPy + Gymnasium

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Usage

model = load_from_hub(repo_id="AminVilan/QLearning-FrozenLake-v1-4x4-NoSlippery", filename="q-learning.pkl")

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🤖 Model Card

🔗 Hugging Face Model Repository:
👉 AminVilan/QLearning-FrozenLake-v1-4x4-NoSlippery

The model includes:

• Trained Q-table (q-learning.pkl)
• Hyperparameters and configuration
• Evaluation script
• Demo notebook to visualize policy and performance

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🧩 Algorithm Description

Q-Learning is a model-free, off-policy algorithm used to find the optimal action-selection policy for any given finite Markov Decision Process (MDP).

The agent updates the Q-values using the Bellman equation.

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🧠 Training Details

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🧾 Results

• ✅ Converged Policy: Optimal deterministic path to goal
• 🏁 Average Reward: 1.00 +/- 0.00
• 📈 Q-table visualization shows strong convergence around the goal states.

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📝 License

This repository is licensed under the GPL License. See the LICENSE file for more details.

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