tic-tac-toe with q-learning
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Updated
Jun 28, 2024 - Python
tic-tac-toe with q-learning
PacmanRL - Reinforcement Learning for Pacman (Q-Learning / SARSA)
QROA: A Black-Box Query-Response Optimization Attack on LLMs
Implementation of Black-Scholes model, Jarrow-Rudd binomial tree model & butterfly spread option strategy
强化学习中文教程(蘑菇书🍄),在线阅读地址:https://datawhalechina.github.io/easy-rl/
Self-Driving Car Reinforcement Learning 🚘🤔🧠
Search work about Q-learning and the Sokoban game.
A Reinforcement Learning project that trains a Double Deep Q-Network to excel in the Chrome Dino Game by dodging obstacles and maximizing its score through iterative learning.
Dynamic programming. Value iteration methods. Monte Carlo controls. Q-learning.
This repository explores the application of three reinforcement learning algorithms—Deep Q-Networks (DQN), Double Deep Q-Networks (DDQN), and Proximal Policy Optimization (PPO)—for playing Super Mario Bros using the OpenAI Gym and nes-py emulator. It includes a comparative analysis of these models.
This project is a Double Deep Q learning Agent that learns to play the dice game Yahtzee
This repository compares two methodologies for music recommendation: Q-learning and Deep Reinforcement Learning (Dueling DQN), applied to a dataset of music tracks with features like genre, artist, and danceability. The goal is to build a system that recommends music based on user preferences.
Julia implementations of temporal difference Reinforcement Learning algorithms like Q-Learning and SARSA
The notebook contents implementation of solution of OpenAI gym Acrobot-v1 problem.
A collection of reinforcement learning algorithm implementations
Clean, Robust, and Unified PyTorch implementation of popular DRL Algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
This is a trained model of a Q Learning agent playing FrozenLake.
Focuses on Reinforcement Learning related concepts, use cases, and learning approaches
In questa repository una collezione di tutorial sulle basi del Reinforcement Learning, sviluppati in Python, interamente in italiano.
Train and test your IA's using these samples in the machine learning field.
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