An efficient reinforcement learning algorithm for learning a strategy for game 2048

Developed By "Perfect Cube" - https://doi.org/10.36948/ijfmr.2025.v07i01.34840

Program created in java with swing interface.

Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.

Year-4 Module taken in NTU that focuses on reinforcement learning algorithms, single intelligent agent and multiagent systems.

Q Learning EV3 Robot Learning How to Walk

Using Burlap RL library templates for a more modern experience with burlap

This project, Reversi (also known as Othello), is a robust implementation of the classic 8x8 board game developed in Java. It is a continuation of my work from a previous programming course and now serves as a platform to integrate and test various Artificial Intelligence strategies.

Experiments for a Q-Based Evolutionary Algorithm (QBEA)

Reinforcement learning experiments

Aplicação ANDROID. Implementação de MACHINE LEARNING.

Maze solver using reinforcement learning methods (value iteration, policy iteration)

Implementation of Reinforcement Learning

Implémentation de l'algorithme the Q-learning avec JAVA : Version séquentielle et version multi-agents