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Flow Chart Generator - With Mermaid.live
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1.4K
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ChatGPTFlow Chart Generator - With Mermaid.live
3.8K
1.4K
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ChatGPTTired 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
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
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