The Most Efficient Temporal Difference Learning Framework for 2048
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Updated
Sep 4, 2024 - C++
The Most Efficient Temporal Difference Learning Framework for 2048
Tabular TD control in MAZE environment using Q-Learning, SARSA, and Expected SARSA
A framework for collision probability distribution estimation via temporal difference learning
University of Tehran-Reinforcement Learning Fall 2022
Some algorithms of Reinforcement Learning implemented by me, in accordance to "Introduction to Reinforcement Learning" by Richard Sutton and Andrew Barto.
My Implementation of the Accelerated Gradient Temporal Difference Learning algorithm in Python
Temporal Difference Learning for the Racetrack problem
Temporal difference learning for 2048
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.
This RL project aims to make robot navigate from start to an end goal. Two cases for continuous and discrete action spaces are implemented.
Course work for CSE 574 Planning and Learning Methods in AI
A Reinforcement Learning library for solving custom environments
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.
This module introduces temporal-difference learning and focuses on how it develops over the ideas of both Monte Carlo methods, and dynamic programming.
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.
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