cyber-security-resources/ai_research/ML_Fundamentals/ai_generated/data/ai_generated_python_scripts/Trust_Region_Policy_Optimization_(TRPO).md

Sure! Below is an example Python script that demonstrates the Trust Region Policy Optimization (TRPO) algorithm using OpenAI Gym's "CartPole-v1" environment:

import gym
import numpy as np
import tensorflow as tf


class PolicyNetwork:
    def __init__(self, state_dim, action_dim, hidden_dim):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.hidden_dim = hidden_dim

        self.states = tf.placeholder(tf.float32, [None, state_dim], name="states")
        self.actions = tf.placeholder(tf.int32, [None], name="actions")
        self.advantages = tf.placeholder(tf.float32, [None], name="advantages")

        self.mean_network = self.build_network(scope="mean")
        self.sample_network = self.build_network(scope="sample")

        self.sampled_actions = self.sample_network(self.states)

        self.mean_weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="mean")
        self.sample_weights = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="sample")

        self.policy_loss = self.compute_policy_loss()
        self.kl_divergence = self.compute_kl_divergence()
        self.gradient = self.compute_gradient()

    def build_network(self, scope):
        with tf.variable_scope(scope):
            hidden_layer = tf.layers.dense(self.states, self.hidden_dim, activation=tf.nn.relu)
            output_layer = tf.layers.dense(hidden_layer, self.action_dim)
            output_probs = tf.nn.softmax(output_layer)

        def network(states):
            feed_dict = {self.states: states}
            sess = tf.get_default_session()
            return sess.run(output_probs, feed_dict=feed_dict)

        return network

    def compute_policy_loss(self):
        indices = tf.range(tf.shape(self.sampled_actions)[0]) * tf.shape(self.sampled_actions)[1] + self.actions
        selected_action_probs = tf.gather(tf.reshape(self.sampled_actions, [-1]), indices)
        ratio = selected_action_probs / tf.stop_gradient(self.mean_network(self.states))
        surrogate_loss = -tf.reduce_mean(ratio * self.advantages)
        return surrogate_loss

    def compute_kl_divergence(self):
        mean_network_probs = self.mean_network(self.states)
        sample_network_probs = tf.stop_gradient(self.sampled_actions)
        return tf.reduce_mean(tf.reduce_sum(mean_network_probs * tf.log(mean_network_probs / sample_network_probs), axis=1))

    def compute_gradient(self):
        grads = tf.gradients(self.policy_loss, self.sample_weights)
        flat_grads = tf.concat([tf.reshape(grad, [-1]) for grad in grads], axis=0)
        return flat_grads


def compute_advantages(rewards, next_value, discount_factor=0.99, gae_lambda=0.95):
    values = np.append(rewards, next_value)
    deltas = rewards + discount_factor * values[1:] - values[:-1]
    advantages = np.zeros_like(rewards)
    for t in reversed(range(len(rewards))):
        delta = deltas[t]
        advantages[t] = delta + discount_factor * gae_lambda * advantages[t+1]
    return advantages


def run_episode(env, policy_network, render=False):
    states, actions, rewards = [], [], []
    state = env.reset()
    while True:
        if render:
            env.render()
        action_probs = policy_network.sample_network(np.expand_dims(state, axis=0))
        action = np.random.choice(len(action_probs[0]), p=action_probs[0])
        next_state, reward, done, _ = env.step(action)

        states.append(state)
        actions.append(action)
        rewards.append(reward)

        state = next_state

        if done:
            break

    return states, actions, rewards


def train(env, policy_network, max_iterations=1000, max_episode_length=1000, cg_iterations=10, delta=0.01):
    optimizer = tf.train.AdamOptimizer(learning_rate=1e-3)
    trainable_variables = tf.trainable_variables()
    grads_placeholder = tf.placeholder(tf.float32, shape=[None])
    flat_grads_and_vars_placeholder = tf.placeholder(tf.float32, shape=[None])

    grads = tf.gradients(policy_network.kl_divergence, trainable_variables)
    grads_placeholder_and_vars = list(zip(grads_placeholder, trainable_variables))
    flat_grads_and_vars_placeholder_and_vars = list(zip(flat_grads_and_vars_placeholder, trainable_variables))

    compute_grads = tf.train.AdamOptimizer(learning_rate=1e-3).apply_gradients(grads_placeholder_and_vars)
    compute_flat_grad = flatten_gradients(grads)
    apply_flat_grad = unflatten_gradients(flat_grads_and_vars_placeholder, trainable_variables)

    sess = tf.InteractiveSession()
    sess.run(tf.global_variables_initializer())

    for iteration in range(max_iterations):
        episode_states, episode_actions, episode_rewards = run_episode(env, policy_network)

        episode_advantages = compute_advantages(episode_rewards, 0)
        episode_mean = np.mean(episode_rewards)
        episode_std = np.std(episode_rewards)

        feed_dict = {
            policy_network.states: np.array(episode_states),
            policy_network.actions: np.array(episode_actions),
            policy_network.advantages: episode_advantages
        }

        # Update policy network
        sess.run(optimizer.minimize(policy_network.policy_loss), feed_dict=feed_dict)

        # Update value function (critic network)
        for _ in range(cg_iterations):
            sess.run(compute_flat_grad, feed_dict=feed_dict)

            flat_grads = sess.run(compute_flat_grad, feed_dict=feed_dict)
            feed_dict[flat_grads_placeholder] = flat_grads

            step_direction = conjugate_gradients(sess, compute_flat_grad, feed_dict)
            step_size = np.sqrt(2 * delta / (np.dot(step_direction, hessian_vector_product(sess, state, compute_flat_grad, feed_dict)) + 1e-8))
            feed_dict[flat_grads_and_vars_placeholder] = step_size * step_direction

            sess.run(apply_flat_grad, feed_dict=feed_dict)

    sess.close()


def conjugate_gradients(sess, compute_flat_grad_fn, feed_dict, cg_iterations=10, residual_tol=1e-10):
    x = np.zeros_like(np.array(feed_dict).flatten())
    b = sess.run(compute_flat_grad_fn, feed_dict=feed_dict)
    r = b.copy()
    p = b.copy()
    rsold = np.dot(r, r)

    for _ in range(cg_iterations):
        Ap = sess.run(compute_flat_grad_fn, feed_dict={x: p})
        alpha = rsold / (np.dot(p, Ap) + 1e-8)
        x += alpha * p
        r -= alpha * Ap
        rsnew = np.dot(r, r)

        if np.sqrt(rsnew) < residual_tol:
            break

        p = r + (rsnew / rsold) * p
        rsold = rsnew

    return x


def hessian_vector_product(sess, state, compute_flat_grad_fn, feed_dict, damping=0.1):
    grads = sess.run(compute_flat_grad_fn, feed_dict=feed_dict)
    constraints = tf.placeholder(tf.float32, shape=[None])
    compute_kl_grads = tf.gradients(policy_network.kl_divergence, trainable_variables)
    gradient_products = tf.reduce_sum(compute_kl_grads * constraints)
    feed_dict.update({constraints: grads})
    return sess.run(gradient_products, feed_dict=feed_dict)


def flatten_gradients(grads):
    flat_grads = []
    for grad in grads:
        flat_grads.append(tf.reshape(grad, [-1]))
    return tf.concat(flat_grads, axis=0)


def unflatten_gradients(grads_placeholder, trainable_variables):
    grads = []
    start = 0
    for var in trainable_variables:
        var_shape = var.shape.as_list()
        var_size = np.prod(var_shape)
        grads.append(tf.reshape(grads_placeholder[start:start+var_size], var_shape))
        start += var_size
    return grads


def main():
    env = gym.make('CartPole-v1')

    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.n
    hidden_dim = 32

    policy_network = PolicyNetwork(state_dim, action_dim, hidden_dim)

    train(env, policy_network, max_iterations=100)

    env.close()


if __name__ == "__main__":
    main()

In this script, the TRPO algorithm is used to optimize a policy network to solve the CartPole-v1 environment from the Gym library. The PolicyNetwork class defines the policy network, and the train function implements the TRPO algorithm to train the network. The compute_advantages, run_episode, conjugate_gradients, hessian_vector_product, flatten_gradients, and unflatten_gradients functions are helper functions used in the training process.

Note that this implementation assumes you have TensorFlow and Gym libraries installed. You may need to install additional dependencies if necessary.