Ebad Sayed·May 14MountainCar with Actor-Critic (A2C) — Continuous Actions from ScratchWhat Are We Building?
Thibaut Kulak·Mar 16Can Large Language Models Imitate Reinforcement Learning Experts?Evidence from 20 Gymnasium environments
Kagan Han Catan·Mar 13Agent’lar Taksi Şoförlüğü Yapabilir Mi?Dil modellerinin hayatımıza girmesinden bu yana Agent konusu neredeyse hiç gündemden düşmemektedir. Mevcut örnekleri incelediğimizde…A response icon1A response icon1
FlipGoms·Jan 24AI Car Racing: Teach AI to DriveTeaching a DQN agent to master the track in Gymnasium’s CarRacing using Python. Reinforcement learning meets the fast lane!
Thinking Loop·Jan 8Mastering Gymnasium: RL Environments That ClickA practical, code-first guide to building, wrapping, vectorizing, and debugging reinforcement learning environments with Gymnasium —…
ZihuiChen·Dec 31, 2025Soft Actor-Critic (SAC) for Prosthetic-Like Control Under DriftA practical Soft Actor-Critic (SAC) tutorial: maximum-entropy off-policy control with motor bias/weakness faults and rehabilitation-style…A response icon1A response icon1
InTowards AIbyHitesh Balegar·Dec 16, 2025How to Train AI to Play Multi-Agent Ludo: A Guide to T-REX and Reward Shaping with Deep Q NetworksA step-by-step tutorial on building a custom Gymnasium environment, preventing reward farming with PBRS, and solving sparse rewards with…
InTowards AIbyRem E·Nov 15, 2025Let’s bring Q-learning to life!Tutorial 9.2: Implementing the Q-Learning Algorithm for Our Maze Problem
InTowards AIbyRem E·Nov 9, 2025Learn SARSA the Easy Way: Your First Temporal Difference AlgorithmTutorial 9.1: Implementing the SARSA Algorithm for Our Maze Problem
InTowards AIbyRem E·Sep 1, 2025Monte Carlo Off-Policy for the Maze ProblemTutorial 8.2: Implementing the Off-Policy MC Method for Our Maze Problem
