Master Reinforcement Learning with Real-world Environment Creation

Table of Contents

1. Introduction
2. Building a Custom Cue Learning Environment
3. Installing OpenCV and Required Libraries
4. Creating the Blob Class
5. Initializing the Cue Table
6. Implementing the Q-Learning Algorithm
7. Visualizing the Environment
8. Training the Agent
9. Saving and Loading the Cue Table
10. Conclusion

Building a Custom Cue Learning Environment

Reinforcement learning is a powerful technique that allows an agent to learn through its interactions with an environment. In this tutorial, we will be building our own custom cue learning environment using OpenAI Gym. This tutorial will guide You through the process of creating the environment, implementing the Q-Learning algorithm, and training the agent to solve the environment.

Installing OpenCV and Required Libraries

To build our custom cue learning environment, we will need to install the OpenCV library. Open a command prompt and run the following command to install the required library:

pip install opencv-python

Next, we will also need to install the NumPy library. Run the following command to install NumPy:

pip install numpy

Lastly, we will need the Python Imaging Library (PIL). Run the following command to install PIL:

pip install pillow

Creating the Blob Class

The Blob class will represent the player, food, and enemy entities in our environment. We will define the class with the necessary attributes and methods to handle movement and interactions between the entities. Here is an example implementation of the Blob class:

class Blob:
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def move(self, x, y):
        self.x += x
        self.y += y

    def __sub__(self, other):
        return (self.x - other.x, self.y - other.y)

    def __str__(self):
        return f"Blob({self.x}, {self.y})"

In this example, the Blob class has attributes for the x and y coordinates, a move method to handle movement, an overloaded subtraction operator to calculate the difference between two blobs, and a STRING representation of the blob for better readability.

Initializing the Cue Table

The cue table is a dictionary that maps observations to actions and their corresponding rewards. By initializing the cue table, we provide a starting point for the Q-Learning algorithm to learn from. Here is an example implementation of initializing the cue table:

def initialize_cue_table(size):
    cue_table = {}

    for i in range(-size+1, size):
        for j in range(-size+1, size):
            for k in range(-size+1, size):
                for l in range(-size+1, size):
                    cue_table[((i, j), (k, l))] = np.random.uniform(-5, 0, 4)

    return cue_table

In this example, we use four nested for loops to generate all possible combinations of observations and actions. For each combination, we assign a random value between -5 and 0 as the initial Q-value.

Implementing the Q-Learning Algorithm

The Q-Learning algorithm allows the agent to learn from its interactions with the environment and update the cue table accordingly. Here is an example implementation of the Q-Learning algorithm for our custom cue learning environment:

def q_learning(size, episodes, epsilon, epsilon_decay, learning_rate, discount):
    # Initialize the cue table
    cue_table = initialize_cue_table(size)

    for episode in range(episodes):
        # Reset the environment and calculate the initial observation
        player = Blob(np.random.randint(0, size), np.random.randint(0, size))
        food = Blob(np.random.randint(0, size), np.random.randint(0, size))
        enemy = Blob(np.random.randint(0, size), np.random.randint(0, size))
        observation = (player - food, player - enemy)

        episode_reward = 0

        for _ in range(200):
            # Choose an action based on the epsilon-greedy policy
            if np.random.random() > epsilon:
                action = np.argmax(cue_table[observation])
            else:
                action = np.random.randint(0, 4)

            # Take the action and calculate the new observation and reward
            player.move(action, action)
            new_observation = (player - food, player - enemy)
            reward = get_reward(player, food, enemy)

            # Update the cue table using the Q-Learning formula
            if reward == FOOD_REWARD:
                new_q = FOOD_REWARD
            elif reward == ENEMY_PENALTY:
                new_q = -ENEMY_PENALTY
            else:
                current_q = cue_table[observation][action]
                max_future_q = np.max(cue_table[new_observation])
                new_q = (1 - learning_rate) * current_q + learning_rate * (reward + discount * max_future_q)

            cue_table[observation][action] = new_q

            episode_reward += reward

            observation = new_observation

            # Stop the episode if the agent gets the food or hits the enemy
            if reward in (FOOD_REWARD, -ENEMY_PENALTY):
                break

        # Decay the epsilon value
        epsilon *= epsilon_decay

        episode_rewards.append(episode_reward)

    return cue_table, episode_rewards

In this example, we loop over the specified number of episodes and for each episode, we reset the environment and calculate the initial observation. We then loop over a fixed number of steps within each episode. In each step, we choose an action Based on the epsilon-greedy policy, take the action, calculate the new observation and reward, and update the cue table using the Q-Learning formula. Finally, we decay the epsilon value and Record the episode rewards for analysis.

Visualizing the Environment

To Visualize the environment and track the agent's progress, we can use the OpenCV library. Here is an example implementation of the visualization function:

def visualize_environment(size, episodes, show_every):
    # Initialize the cue table and episode rewards
    cue_table, episode_rewards = q_learning(size, episodes, epsilon, epsilon_decay, learning_rate, discount)

    # Create the base environment grid
    environment = np.zeros((size, size, 3), dtype=np.uint8)

    for episode in range(episodes):
        player = Blob(np.random.randint(0, size), np.random.randint(0, size))
        food = Blob(np.random.randint(0, size), np.random.randint(0, size))
        enemy = Blob(np.random.randint(0, size), np.random.randint(0, size))

        episode_reward = 0

        for _ in range(200):
            # Choose an action based on the cue table
            observation = (player - food, player - enemy)
            action = np.argmax(cue_table[observation])

            player.move(action, action)

            reward = get_reward(player, food, enemy)

            episode_reward += reward

            # Stop the episode if the agent gets the food or hits the enemy
            if reward in (FOOD_REWARD, -ENEMY_PENALTY):
                break

        episode_rewards.append(episode_reward)

        # Show the environment every few episodes
        if episode % show_every == 0:
            environment[player.y][player.x] = BLUE
            environment[food.y][food.x] = GREEN
            environment[enemy.y][enemy.x] = RED

            image = Image.fromarray(environment)
            image = image.resize((300, 300))
            image.show()

        # Check if the simulation ended
        if reward in (FOOD_REWARD, -ENEMY_PENALTY):
            time.sleep(0.5)

        # Update the moving average of episode rewards
        moving_average = np.convolve(episode_rewards, np.ones(show_every), 'valid') / show_every

        # Plot the moving average of episode rewards
        plt.plot(moving_average, label="Moving Average")
        plt.xlabel("Episode Number")
        plt.ylabel("Reward")
        plt.show()

        # Save the cue table
        with open(f"cue_table_{int(time.time())}.pickle", "wb") as f:
            pickle.dump(cue_table, f)

In this example, we loop over the specified number of episodes and for each episode, we reset the environment and calculate the initial observation. We then loop over a fixed number of steps within each episode and choose an action based on the cue table. We update the environment GRID based on the agent's position and Show the environment every few episodes. Finally, we calculate the moving average of episode rewards and plot it on a graph for analysis.

Training the Agent

To train the agent in our custom cue learning environment, we can call the visualize_environment function with the desired parameters. Here is an example implementation of training the agent:

size = 10
episodes = 10000
show_every = 1000

visualize_environment(size, episodes, show_every)

In this example, we set the size of the environment grid to 10x10, the number of episodes to 10,000, and the show_every parameter to 1,000. This means that the environment will be shown every 1,000 episodes, and the progress of the agent will be visualized on a graph.

Saving and Loading the Cue Table

Once the agent has been trained, we can save the cue table to a file for future use. We can also load a pre-trained cue table from a file to Continue training or use it for evaluation. Here is an example of saving and loading the cue table:

# Save the cue table
with open(f"cue_table_{int(time.time())}.pickle", "wb") as f:
    pickle.dump(cue_table, f)

# Load a pre-trained cue table
with open("pretrained_cue_table.pickle", "rb") as f:
    cue_table = pickle.load(f)

In these examples, we use the pickle library to serialize and deserialize the cue table object. We open a file in binary write mode to save the cue table, and we open a file in binary Read mode to load a pre-trained cue table.

Conclusion

In this tutorial, we have learned how to build a custom cue learning environment using OpenAI Gym. We have implemented the Q-Learning algorithm and trained the agent to solve the environment. We have also visualized the environment and tracked the agent's progress using OpenCV and matplotlib. By saving and loading the cue table, we can continue training or evaluate the agent's performance. The custom cue learning environment offers endless possibilities for experimentation and further exploration of reinforcement learning algorithms.