Reinforcement Learning: How AI Teaches Itself to Win

Reinforcement Learning (RL) enables AI to improve through trial and error.

AI learns by interacting with an environment and receiving rewards for good actions.

Example: Training an RL Agent in OpenAI Gym

import gym

env = gym.make("CartPole-v1")
state = env.reset()

for _ in range(1000):
    action = env.action_space.sample()
    state, reward, done, _ = env.step(action)
    if done:
        break

env.close()

This script runs a random agent in the “CartPole” environment.