Policy Gradient Code Example

This section provides an example of code for implementing the Policy Gradient algorithm in deep learning. The Policy Gradient is a popular reinforcement learning algorithm used to train agents to make decisions based on their environment.

Overview

The Policy Gradient algorithm is based on the principle of maximizing the expected return by adjusting the policy parameter. The algorithm learns a policy that maps states to actions, and then uses this policy to guide the agent's behavior.

Code Structure

The following is a simplified example of how you might implement a Policy Gradient algorithm using Python and TensorFlow:

import tensorflow as tf
import numpy as np

# Define the environment
class Environment:
    def __init__(self):
        # Initialize environment parameters
        pass

    def step(self, action):
        # Execute action and return next state, reward, and done flag
        pass

# Define the policy network
class PolicyNetwork(tf.keras.Model):
    def __init__(self, state_size, action_size):
        super(PolicyNetwork, self).__init__()
        self.fc1 = tf.keras.layers.Dense(64, activation='relu')
        self.fc2 = tf.keras.layers.Dense(64, activation='relu')
        self.fc3 = tf.keras.layers.Dense(action_size, activation='softmax')

    def call(self, state):
        x = self.fc1(state)
        x = self.fc2(x)
        x = self.fc3(x)
        return x

# Define the Policy Gradient algorithm
def policy_gradient(env, policy_network, num_episodes=1000):
    for episode in range(num_episodes):
        state = env.reset()
        done = False
        total_reward = 0

        while not done:
            action_probs = policy_network(state)
            action = np.random.choice(range(action_probs.shape[1]), p=action_probs.ravel())
            next_state, reward, done = env.step(action)
            total_reward += reward

            # Update the policy network
            with tf.GradientTape() as tape:
                action_probs = policy_network(state)
                log_probs = tf.math.log(action_probs)
                loss = -log_probs[0, action] * reward

            gradients = tape.gradient(loss, policy_network.trainable_variables)
            policy_network.optimizer.apply_gradients(zip(gradients, policy_network.trainable_variables))

        print(f"Episode {episode + 1}: Total Reward = {total_reward}")

# Example usage
if __name__ == "__main__":
    env = Environment()
    policy_network = PolicyNetwork(state_size=4, action_size=2)
    policy_gradient(env, policy_network)

Policy Gradient Code Example

Overview

Code Structure

Further Reading