Trust Region Policy Optimization (TRPO)
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.