The Algorithm Everyone Skips
Most RL tutorials rush you into DQN, PPO, or some other three-letter acronym before you've seen a Q-table update in action. Then you're stuck debugging gradient explosions without understanding why the robot keeps running into walls.
Q-Learning is the algorithm everyone should write once. Not because it scales (it doesn't), but because it's the only RL method you can fit in your head completely. You can print the entire Q-table, watch it converge, and understand exactly why your agent just learned to avoid the cliff.
Here's a working implementation in 50 lines that solves FrozenLake-v1. Then we'll break down what actually happens during training.
The Full Implementation
python
import gymnasium as gym
import numpy as np
np.random.seed(42)
# Environment setup
env = gym.make('FrozenLake-v1', is_slippery=True)
n_states = env.observation_space.n
n_actions = env.action_space.n
# Q-table: states × actions, initialized to zeros
Q = np.zeros((n_states, n_actions))
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*Continue reading the full article on [TildAlice](https://tildalice.io/q-learning-from-scratch-50-line-agent/)*
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