A set of AIs for the 2048 tile-merging game. Includes an expectimax strategy that reaches 16384 with 34.6% success and an ML model trained with temporal difference learning.
The Most Efficient Temporal Difference Learning Framework for 2048
Towards explainable value functions in reinforcement learning. A framework for collision probability distribution estimation via deep temporal difference learning.
Solutions for course: "Applied Game Theory" taken at University of Novi Sad - Faculty of Technical Sciences
A reinforcement learning framework for the game of Nim.
Temporal difference learning for 2048
Some algorithms of Reinforcement Learning implemented by me, in accordance to "Introduction to Reinforcement Learning" by Richard Sutton and Andrew Barto.
This module introduces temporal-difference learning and focuses on how it develops over the ideas of both Monte Carlo methods, and dynamic programming.
My Implementation of the Accelerated Gradient Temporal Difference Learning algorithm in Python
Course work for CSE 574 Planning and Learning Methods in AI
Q-learning and SARSA playing ludo.
Tabular TD control in MAZE environment using Q-Learning, SARSA, and Expected SARSA
Roskilde University, 2nd semester project (AI - Reinforcement Learning)
This repository contains my undergraduate thesis source code for Multi-stage Temporal Difference Learning with 2048 as an AI testbed. I reimagined my original C++ implementation in Qt for visualisation purposes.
A Reinforcement Learning library for solving custom environments
SuPeReInFoRzE is a cutting-edge RL framework for Super TTT. The system combines multiple AI paradigms to create agents capable of human-expert level play through: ⚡ Temporal Difference Learning (Q-Learning with Experience Replay) 🌲 Monte Carlo Tree Search🎯 Minimax with Alpha-Beta Pruning 🧮 Advanced Heuristic Evaluation
University of Tehran-Reinforcement Learning Fall 2022
Q-Learning Implementation for Process Optimization A reinforcement learning project that calculates the shortest route between locations using the Q-Learning algorithm. This code demonstrates how AI can optimize processes in a simulated environment with predefined states and rewards. 🚀
This RL project aims to make robot navigate from start to an end goal. Two cases for continuous and discrete action spaces are implemented.
Temporal Difference Learning for the Racetrack problem
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