q-table

Deep Reinforcement Learning for mobile robot navigation, a robot learns to navigate to a random goal point from random moves to adopting a strategy, in a simulated maze environment while avoiding dynamic obstacles.

Sample projects to learn reinforcement learning and deep reinforcement learning in practice.

Tic-Tac-Toe Q-Learning is a beginner-friendly example of using reinforcement learning.

This project proposes a simulation-based reinforcement learning framework for safe medicine dosage optimization. A virtual patient environment is mathematically modeled to simulate infection progression, toxicity accumulation, and immunity dynamics.

A command-line turn‑based arena where you face an AI that learns with Q‑Learning: the agent buckets battle states into a Q‑table, updates action values after each fight using rewards and an epsilon‑greedy policy. It is ideal for experimenting with reinforcement learning, tweaking strategies and teaching core RL concepts through hands‑on pl

In this Assignment, you will teach RL Agents to pickup packages on a grid-world. The environment you will be using is called the Four-Rooms domain

MuJoCo-based reinforcement learning with Q-learning-driven curriculum for robust humanoid locomotion under perturbations.

A 1vs1 game using reinforcement learning

"Multi-Environment RL Benchmark: Optimizing Q-Learning via Reward Shaping & Hyperparameter Tuning" / "다중 환경 강화학습 벤치마크: 보상 설계(Reward Shaping) 및 하이퍼파라미터 튜닝을 통한 Q-Learning 최적화"

Introduction to Reinforcement learning without math overhead.

An interactive Flappy Bird game combining Reinforcement Learning, OpenGL graphics, and classic computer graphics algorithms, where an AI agent learns to survive through continuous self-play.

An implementation of iterative prisoner dilemma with reinforcement learning via q_table (NOT DQN) in Python with around 30 strategies

This is a machine learning project developed at woodhack 2018 - demonstrating that machine learning can be done from scratch, even without a framework.

This is an introductory tutorial to tabular Q-learning in Norwegian.

Tabular Q-learning agent for tic-tac-toe. Trained against a uniformly-random opponent; no minimax, no hard-coded rules.

Implementation of core reinforcement learning algorithms with PyTorch

42 project that involves implementing Reinforcement Learning

Q learning algorithm example in python

An interactive 3D multi-agent learning sandbox built as a single HTML file. Agents navigate a bounded box, gather objectives, avoid threats, use shelters, adapt to gravity anomalies, exchange learned values, and enter counterfactual replay portals that create alternative simulated branches.

We use python software and NumPy library to implement the Q-learning method , train an Agent to solve a Reinforcement Learning Problem