Reinforcement Learning: How AI Teaches Itself to Win

Reinforcement Learning: How AI Teaches Itself to Win-thumb-nail

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.