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🕹️ Reinforcement Learning with OpenAI Gym

In this tutorial, we explore Reinforcement Learning (RL) using Q-learning with OpenAI Gym. We'll train an agent to play the FrozenLake game using Python.

📦 Prerequisites

Basic Python knowledge
Install required libraries: pip install gym numpy
Conceptual knowledge of RL (states, actions, rewards)

🎯 Step-by-Step Q-Learning with FrozenLake

Step 1: Import Libraries


import gym

import numpy as np

import random

Step 2: Initialize Environment


env = gym.make("FrozenLake-v1", is_slippery=False)

state_size = env.observation_space.n

action_size = env.action_space.n

q_table = np.zeros((state_size, action_size))

Step 3: Define Hyperparameters


total_episodes = 10000

learning_rate = 0.8

max_steps = 100

gamma = 0.95  # Discount rate

epsilon = 1.0

max_epsilon = 1.0

min_epsilon = 0.01

decay_rate = 0.005

Step 4: Train the Agent


for episode in range(total_episodes):

  state = env.reset()[0]

  done = False

  for step in range(max_steps):

    exp_exp_tradeoff = random.uniform(0,1)

    if exp_exp_tradeoff > epsilon:

      action = np.argmax(q_table[state,:])

    else:

      action = env.action_space.sample()


    new_state, reward, done, truncated, _ = env.step(action)

    q_table[state, action] = q_table[state, action] + learning_rate * (reward + gamma * np.max(q_table[new_state, :]) - q_table[state, action])

    state = new_state

    if done:

      break

  epsilon = min_epsilon + (max_epsilon - min_epsilon) * np.exp(-decay_rate * episode)

Step 5: Evaluate the Agent


total_rewards = 0

for episode in range(100):

  state = env.reset()[0]

  done = False

  for step in range(max_steps):

    action = np.argmax(q_table[state,:])

    new_state, reward, done, truncated, _ = env.step(action)

    total_rewards += reward

    state = new_state

    if done:

      break

print("Average reward:", total_rewards / 100)

📌 Tips

Use slippery=True for harder environments
Try other environments like CartPole-v1 and MountainCar-v0
Consider Deep Q-Learning with Neural Networks for large state spaces

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🎯 Summary

Reinforcement Learning allows an agent to learn optimal behavior through reward signals. Using OpenAI Gym makes experimentation easy and visual.

🔜 Coming Soon:

Build Your Own Neural Network from Scratch in Python (No Libraries!)