Similar to given code below, write complete and in details code for clash resolution for MEP in Revit and show how to test actor  :
import gym
import tensorflow as tf
from tensorflow.keras import layers
import numpy as np
import matplotlib.pyplot as plt

problem = “Pendulum-v1”
env = gym.make(problem)

num_states = env.observation_space.shape[0]
print(“Size of State Space -> {}”.format(num_states))
num_actions = env.action_space.shape[0]
print(“Size of Action Space -> {}”.format(num_actions))

upper_bound = env.action_space.high[0]
lower_bound = env.action_space.low[0]

print(“Max Value of Action -> {}”.format(upper_bound))
print(“Min Value of Action -> {}”.format(lower_bound))

class OUActionNoise:
def init(self, mean, std_deviation, theta=0.15, dt=1e-2, x_initial=None):
self.theta = theta
self.mean = mean
self.std_dev = std_deviation
self.dt = dt
self.x_initial = x_initial
self.reset()

def call(self):
# Formula taken from https://www.wikipedia.org/wiki/Ornstein-Uhlenbeck_process.
x = (
self.x_prev
+ self.theta * (self.mean - self.x_prev) * self.dt
+ self.std_dev * np.sqrt(self.dt) * np.random.normal(size=self.mean.shape)
)
# Store x into x_prev
# Makes next noise dependent on current one
self.x_prev = x
return x

def reset(self):
if self.x_initial is not None:
self.x_prev = self.x_initial
else:
self.x_prev = np.zeros_like(self.mean)


class Buffer:
def init(self, buffer_capacity=100000, batch_size=64):
# Number of “experiences” to store at max
self.buffer_capacity = buffer_capacity
# Num of tuples to train on.
self.batch_size = batch_size

# Its tells us num of times record() was called.
self.buffer_counter = 0

# Instead of list of tuples as the exp.replay concept go
# We use different np.arrays for each tuple element
self.state_buffer = np.zeros((self.buffer_capacity, num_states))
self.action_buffer = np.zeros((self.buffer_capacity, num_actions))
self.reward_buffer = np.zeros((self.buffer_capacity, 1))
self.next_state_buffer = np.zeros((self.buffer_capacity, num_states))

# Takes (s,a,r,s’) obervation tuple as input
def record(self, obs_tuple):
# Set index to zero if buffer_capacity is exceeded,
# replacing old records
index = self.buffer_counter % self.buffer_capacity

self.state_buffer[index] = obs_tuple[0]
self.action_buffer[index] = obs_tuple[1]
self.reward_buffer[index] = obs_tuple[2]
self.next_state_buffer[index] = obs_tuple[3]

self.buffer_counter += 1

# Eager execution is turned on by default in TensorFlow 2. Decorating with tf.function allows
# TensorFlow to build a static graph out of the logic and computations in our function.
# This provides a large speed up for blocks of code that contain many small TensorFlow operations such as this one.
@tf.function
def update(
self, state_batch, action_batch, reward_batch, next_state_batch,
):
# Training and updating Actor & Critic networks.
# See Pseudo Code.
with tf.GradientTape() as tape:
target_actions = target_actor(next_state_batch, training=True)
y = reward_batch + gamma * target_critic(
[next_state_batch, target_actions], training=True
)
critic_value = critic_model([state_batch, action_batch], training=True)
critic_loss = tf.math.reduce_mean(tf.math.square(y - critic_value))

critic_grad = tape.gradient(critic_loss, critic_model.trainable_variables)
critic_optimizer.apply_gradients(
zip(critic_grad, critic_model.trainable_variables)
)

with tf.GradientTape() as tape:
actions = actor_model(state_batch, training=True)
critic_value = critic_model([state_batch, actions], training=True)
# Used -value as we want to maximize the value given
# by the critic for our actions
actor_loss = -tf.math.reduce_mean(critic_value)

actor_grad = tape.gradient(actor_loss, actor_model.trainable_variables)
actor_optimizer.apply_gradients(
zip(actor_grad, actor_model.trainable_variables)
)

# We compute the loss and update parameters
def learn(self):
# Get sampling range
record_range = min(self.buffer_counter, self.buffer_capacity)
# Randomly sample indices
batch_indices = np.random.choice(record_range, self.batch_size)

# Convert to tensors
state_batch = tf.convert_to_tensor(self.state_buffer[batch_indices])
action_batch = tf.convert_to_tensor(self.action_buffer[batch_indices])
reward_batch = tf.convert_to_tensor(self.reward_buffer[batch_indices])
reward_batch = tf.cast(reward_batch, dtype=tf.float32)
next_state_batch = tf.convert_to_tensor(self.next_state_buffer[batch_indices])

self.update(state_batch, action_batch, reward_batch, next_state_batch)


# This update target parameters slowly
# Based on rate tau, which is much less than one.
@tf.function
def update_target(target_weights, weights, tau):
for (a, b) in zip(target_weights, weights):
a.assign(b * tau + a * (1 - tau))


def get_actor():
# Initialize weights between -3e-3 and 3-e3
last_init = tf.random_uniform_initializer(minval=-0.003, maxval=0.003)

inputs = layers.Input(shape=(num_states,))
out = layers.Dense(256, activation=“relu”)(inputs)
out = layers.Dense(256, activation=“relu”)(out)
outputs = layers.Dense(1, activation=“tanh”, kernel_initializer=last_init)(out)

# Our upper bound is 2.0 for Pendulum.
outputs = outputs * upper_bound
model = tf.keras.Model(inputs, outputs)
return model


def get_critic():
# State as input
state_input = layers.Input(shape=(num_states))
state_out = layers.Dense(16, activation=“relu”)(state_input)
state_out = layers.Dense(32, activation=“relu”)(state_out)

# Action as input
action_input = layers.Input(shape=(num_actions))
action_out = layers.Dense(32, activation=“relu”)(action_input)

# Both are passed through seperate layer before concatenating
concat = layers.Concatenate()([state_out, action_out])

out = layers.Dense(256, activation=“relu”)(concat)
out = layers.Dense(256, activation=“relu”)(out)
outputs = layers.Dense(1)(out)

# Outputs single value for give state-action
model = tf.keras.Model([state_input, action_input], outputs)

return model


def policy(state, noise_object):
sampled_actions = tf.squeeze(actor_model(state))
noise = noise_object()
# Adding noise to action
sampled_actions = sampled_actions.numpy() + noise

# We make sure action is within bounds
legal_action = np.clip(sampled_actions, lower_bound, upper_bound)

return [np.squeeze(legal_action)]


std_dev = 0.2
ou_noise = OUActionNoise(mean=np.zeros(1), std_deviation=float(std_dev) * np.ones(1))

actor_model = get_actor()
critic_model = get_critic()

target_actor = get_actor()
target_critic = get_critic()

# Making the weights equal initially
target_actor.set_weights(actor_model.get_weights())
target_critic.set_weights(critic_model.get_weights())

# Learning rate for actor-critic models
critic_lr = 0.002
actor_lr = 0.001

critic_optimizer = tf.keras.optimizers.Adam(critic_lr)
actor_optimizer = tf.keras.optimizers.Adam(actor_lr)

total_episodes = 100
# Discount factor for future rewards
gamma = 0.99
# Used to update target networks
tau = 0.005

buffer = Buffer(50000, 64)


# To store reward history of each episode
ep_reward_list = []
# To store average reward history of last few episodes
avg_reward_list = []

# Takes about 4 min to train
for ep in range(total_episodes):

prev_state = env.reset()
episodic_reward = 0

while True:
# Uncomment this to see the Actor in action
# But not in a python notebook.
# env.render()

tf_prev_state = tf.expand_dims(tf.convert_to_tensor(prev_state), 0)

action = policy(tf_prev_state, ou_noise)
# Recieve state and reward from environment.
state, reward, done, info = env.step(action)

buffer.record((prev_state, action, reward, state))
episodic_reward += reward

buffer.learn()
update_target(target_actor.variables, actor_model.variables, tau)
update_target(target_critic.variables, critic_model.variables, tau)

# End this episode when done is True
if done:
break

prev_state = state

ep_reward_list.append(episodic_reward)

# Mean of last 40 episodes
avg_reward = np.mean(ep_reward_list[-40:])
print(“Episode * {} * Avg Reward is ==> {}”.format(ep, avg_reward))
avg_reward_list.append(avg_reward)

# Plotting graph
# Episodes versus Avg. Rewards
plt.plot(avg_reward_list)
plt.xlabel(“Episode”)
plt.ylabel(“Avg. Epsiodic Reward”)
plt.show()


# Save the weights
actor_model.save_weights(“pendulum_actor.h5”)
critic_model.save_weights(“pendulum_critic.h5”)

target_actor.save_weights(“pendulum_target_actor.h5”)
target_critic.save_weights(“pendulum_target_critic.h5”)