Reinforcement Learning Algorithms in a simple Gridworld
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Updated
May 8, 2024 - Jupyter Notebook
Reinforcement Learning Algorithms in a simple Gridworld
Modern applied deep learning with reinforcement methodology.
Made with the gym package from the farama foundation, this project is an hyper detailed version of the Q-Learning reinforcement on the Frozen lake's game.
Implementation of Q-learning Algorithm on FrozenLake and Taxi environments
This repo implements Deep Q-Network (DQN) for solving the Frozenlake-v1 environment of the Gymnasium library using Python 3.8 and PyTorch 2.0.1 in both 4x4 and 8x8 map sizes.
Using the OpenAI Gym library, I implemented two reinforcement learning algorithms in the Frozen Lake environment.
The cooler AI semester
Implementation of RL Algorithms in Openai Gym Frozen-Lake Environment
Solving Frozen Lake Problem with Q-learning
Project for my KTA ( IM0702-232411M) course In our project, we are tasked with learning an agent to traverse a frozen lake without falling into the water. The agent learns by trial-and-error, adjusting the actions it takes based on the rewards it received in the past.
This project aims to explore the basic concepts of Reinforcement Learning using the FrozenLake environment from the OpenAI Gym library.
This repository showcases the implementation of a Double Deep Q-Learnig algorithm for the FrozenLake environment from Open AI's gym library.
Frozen Lake Q-Learning RL
using Q-learning in 2 environment
solving a simple 4*4 Gridworld almost similar to openAI gym FrozenLake using SARSA Temporal difference method Reinforcement Learning
During my Course of Ai in kiet. I was learing reinforce learning algorithm. I have implemented Q-Learnning on Frozen Lake. Great game/ also make ppt for describe code
Implementation of tabular Reinforcement Learning methods to navigate an agent to reach the goal through any custom N by M FrozenLake grid .
Coding Challenge: Build the FrozenLake Environment from OpenAI Gym using Jax
A Reinforcement Learning course with classic examples of agents trained on gym environments.
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