What is reinforcement learning in Python?

What is reinforcement learning in Python?

Reinforcement learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward.

Can you give me an example of a reinforcement learning algorithm?

Sure! One popular algorithm is Q-learning, which helps an agent learn the value of actions in different states to optimize its strategy.

How do I implement Q-learning in Python?

You can implement Q-learning using libraries like NumPy for calculations and OpenAI's Gym for creating environments. Would you like a code snippet?

Yes, please provide a simple code example.

Here's a basic example:

import numpy as np
import gym

# Create environment
env = gym.make('Taxi-v3')

# Initialize Q-table
Q = np.zeros([env.observation_space.n, env.action_space.n])

# Q-learning parameters
learning_rate = 0.1
discount_factor = 0.9
num_episodes = 1000

# Training loop
for episode in range(num_episodes):
    state = env.reset()
    done = False
    while not done:
        action = np.argmax(Q[state, :] + np.random.randn(1, env.action_space.n) * (1. / (episode + 1)))
        next_state, reward, done, _ = env.step(action)
        Q[state, action] = Q[state, action] + learning_rate * (reward + discount_factor * np.max(Q[next_state, :]) - Q[state, action])
        state = next_state