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frozen-lake-q-agent – AI Model by jatinror | AlphaNeural AI

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jatinror/
frozen-lake-q-agent

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Frozen-Lake-v1 Q-Learning Agent

This repository contains a trained Q-learning agent for the Frozen-Lake-v1 environment from Gymnasium.

The agent is trained to navigate the frozen lake, avoid holes, and reach the goal efficiently. It uses a Q-table stored in model.zip.

Environment

Name: FrozenLake-v1
Observation Space: Discrete (depends on lake size, e.g., 6x6 → 36 states)
Action Space: Discrete 4 (LEFT, DOWN, RIGHT, UP)
Map: Randomized frozen lake with holes (H) and goal (G)
Slippery: True (agent may slide on ice)

Algorithm

Type: Q-learning (tabular)
Hyperparameters:
- Learning rate: 0.7
- Discount factor (gamma): 0.95
- Exploration (epsilon): decaying from 1.0 → 0.01
Episodes: 8000+
Max Steps per Episode: 200

Model

The trained Q-table is saved as:

Download model.zip

Inside model.zip, you will find q_table.npy.

How to Load the Model

python

import numpy as np

# Load Q-table
q_table = np.load("model.zip", allow_pickle=True)

# Example usage
state = 0  # starting state
action = np.argmax(q_table[state, :])