Flow Chart Generator - With Mermaid.live

Help me to write the logic in the following program in into mermaid.live input to generate sequenceDiagram: { code: import numpy as np import tensorflow as tf from tensorflow.keras import layers import gym # Hyperparameters GAMMA = 0.99 TAU = 0.005 BATCH_SIZE = 64 BUFFER_SIZE = 1000000 ACTOR_LR = 0.001 CRITIC_LR = 0.002 class ReplayBuffer: def __init__(self, buffer_size): self.buffer_size = buffer_size self.buffer = [] self.position = 0 def add(self, state, action, reward, next_state, done): transition = (state, action, reward, next_state, done) if len(self.buffer) < self.buffer_size: self.buffer.append(transition) else: self.buffer[self.position] = transition self.position = (self.position + 1) % self.buffer_size def sample(self, batch_size): indices = np.random.choice(len(self.buffer), size=batch_size) return [self.buffer[i] for i in indices] def __len__(self): return len(self.buffer) class DDPG: def __init__(self, state_dim, action_dim, max_action): self.actor = self.create_actor(state_dim, action_dim, max_action) self.actor_target = self.create_actor(state_dim, action_dim, max_action) self.actor_target.set_weights(self.actor.get_weights()) self.critic = self.create_critic(state_dim, action_dim) self.critic_target = self.create_critic(state_dim, action_dim) self.critic_target.set_weights(self.critic.get_weights()) self.actor_optimizer = tf.keras.optimizers.Adam(learning_rate=ACTOR_LR) self.critic_optimizer = tf.keras.optimizers.Adam(learning_rate=CRITIC_LR) def create_actor(self, state_dim, action_dim, max_action): inputs = layers.Input(shape=(state_dim,)) x = layers.Dense(400, activation='relu')(inputs) x = layers.Dense(300, activation='relu')(x) x = layers.Dense(action_dim, activation='tanh')(x) outputs = max_action * x return tf.keras.Model(inputs=inputs, outputs=outputs) def create_critic(self, state_dim, action_dim): state_inputs = layers.Input(shape=(state_dim,)) action_inputs = layers.Input(shape=(action_dim,)) x = layers.Concatenate()([state_inputs, action_inputs]) x = layers.Dense(400, activation='relu')(x) x = layers.Dense(300, activation='relu')(x) outputs = layers.Dense(1)(x) return tf.keras.Model(inputs=[state_inputs, action_inputs], outputs=outputs) def train(self, replay_buffer): sample = replay_buffer.sample(BATCH_SIZE) state, action, reward, next_state, done = list(map(np.array, zip(*sample))) with tf.GradientTape() as tape: target_actions = self.actor_target(next_state) target_q_values = self.critic_target([next_state, target_actions]) target_values = reward + GAMMA * target_q_values * (1 - done) q_values = self.critic([state, action]) critic_loss = tf.reduce_mean((q_values - target_values) ** 2) critic_grads = tape.gradient(critic_loss, self.critic.trainable_variables) self.critic_optimizer.apply_gradients(zip(critic_grads, self.critic.trainable_variables)) with tf.GradientTape() as tape: actions = self.actor(state) actor_loss = -tf.reduce_mean(self.critic([state, actions])) actor_grads = tape.gradient(actor_loss, self.actor.trainable_variables) self.actor_optimizer.apply_gradients(zip(actor_grads, self.actor.trainable_variables)) # Update target networks self.update_target_networks() def update_target_networks(self): actor_weights = self.actor.get_weights() actor_target_weights = self.actor_target.get_weights() critic_weights = self.critic.get_weights() critic_target_weights = self.critic_target.get_weights() for i in range(len(actor_weights)): actor_target_weights[i] = TAU * actor_weights[i] + (1 - TAU) * actor_target_weights[i] for i in range(len(critic_weights)): critic_target_weights[i] = TAU * critic_weights[i] + (1 - TAU) * critic_target_weights[i] self.actor_target.set_weights(actor_target_weights) self.critic_target.set_weights(critic_target_weights) def select_action(self, state): state = np.expand_dims(state, axis=0) return self.actor(state).numpy().flatten() agent = DDPG(state_dim, action_dim, max_action) replay_buffer = ReplayBuffer(BUFFER_SIZE) episode_rewards = [] for episode in range(1, 101): state = env.reset() episode_reward = 0 for t in range(1, 201): action = agent.select_action(state) next_state, reward, done, _ = env.step(action) replay_buffer.add(state, action, reward, next_state, done) if len(replay_buffer) >= BATCH_SIZE: agent.train(replay_buffer) state = next_state episode_reward += reward if done: break }