Core AI Algorithms, Generative Models, NLP, and Computer Vision
Q-Learning Algorithm
This algorithm uses a value-based approach for discrete state-action spaces. The update rule follows Bellman equation:
$$ Q(s,a) \leftarrow Q(s,a) + \alpha [r + \gamma \max_{a'} Q(s',a') - Q(s,a)] $$ Implementation example using OpenAI Gym:
import numpy as np
import gym
env = gym.make('CartPole-v1')
state_space = env.obse ...
Posted on Wed, 23 Sep 2026 16:20:38 +0000 by ryanbutler