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# Flow Chart Generator - With Mermaid.live | [Start Chat](https://gptcall.net/chat.html?data=%7B%22contact%22%3A%7B%22id%22%3A%22uzZ_YXqA5Jt8PZTFzD9xR%22%2C%22flow%22%3Atrue%7D%7D)
Tired of preparing presentations and slides? Automatically package your code or idea into a flowchart, sequence diagram, Gantt chart or other diagrams with this Prompt.



Based on online editor of Mermaid.live (https://mermaid.live/)



The generated results from ChatGPT will be input to Mermaid.live flowchart generator, which outputs nice plot and looks like the format below:

sequenceDiagram

    participant env as Environment

    participant main

    participant rb as ReplayBuffer

    participant agent as DDPG Agent



    main->>agent: Initialize DDPG Agent

    main->>rb: Initialize ReplayBuffer

    loop Episode 1 to 100

        main->>env: Reset environment

        loop Time step 1 to 200

            main->>agent: Select action

            main->>env: Perform action

            env-->>main: next_state, reward, done

            main->>rb: Add experience to ReplayBuffer



            opt Training

                main->>agent: Train

                agent->>rb: Sample from ReplayBuffer

                rb-->>agent: state, action, reward, next_state, done

                

                agent->>agent: Compute critic_loss and actor_loss

                agent->>agent: Update critic and actor weights

                agent->>agent: Update target networks

            end



            main->>main: Update state and episode_reward

            opt Episode ends

                main->>main: Break

            end

        end

        main->>main: Record episode reward

    end

# Prompt

```
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
}
```

## Conversation

**ASSISTANT**: Here is the mermaid code for the sequence diagram of the given program:



```

sequenceDiagram

    participant main_program

    participant ReplayBuffer

    participant DDPG

    participant gym

    participant tf.GradientTape

    participant actor_optimizer

    participant critic_optimizer



    main_program->>+ReplayBuffer: Add to buffer

    main_program->>+DDPG: Select action

    DDPG->>+gym: Get next state, reward

    DDPG->>-ReplayBuffer: Sample from buffer

    DDPG->>tf.GradientTape: Compute critic loss

    tf.GradientTape->>-DDPG: Update critic weights

    DDPG->>tf.GradientTape: Compute actor loss

    tf.GradientTape->>-DDPG: Update actor weights

    DDPG->>actor_optimizer: Apply actor gradients

    actor_optimizer->>-DDPG: Update actor weights

    DDPG->>critic_optimizer: Apply critic gradients

    critic_optimizer->>-DDPG: Update critic weights

    DDPG->>+DDPG: Update target networks

    DDPG-->>-main_program: Episode reward

```



The above diagram represents the flow of control between different objects/classes in the program. It starts with the main program sending the state, action, reward, and next_state to the ReplayBuffer object. Then, the DDPG object selects an action based on the current state and sends it to the gym environment to get the next state and reward.



Next, the DDPG object samples a batch of transitions from the ReplayBuffer object and uses them to compute the critic loss and actor loss using GradientTape. The GradientTape object computes the gradients of the loss functions with respect to the trainable variables of the critic and actor models.



After that, the actor_optimizer and critic_optimizer objects apply the gradients to update the actor and critic models' weights. Finally, the DDPG object updates the target networks using a soft update strategy.



In the end, the episode reward is sent back to the main program.


