Action Introduction
The action module is responsible for translating the agent’s decisions into specific outcomes. This module is located at the most downstream position and directly interacts with the environment. It is influenced by the profile, memory, and planning modules.
Actions Overview
In DB-GPT, any agent must have an action.
There are four perspectives according the paper A survey on large language model based autonomous agents:
Action Goals
What the agent wants to achieve with the action?
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Task Completion: Complete specific tasks, write a function in software development, and make an iron pick in the game.
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Communication: Communicate with other agents.
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Environment exploration: Explore unfamiliar environments to expand its perception and strike a balance between exploring and exploiting.
Action Production
How are the actions generated?
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Action via memory recollection. In this strategy, the action is generated by extracting information from the agent memory according to the current task. The task and the extracted memories are used as prompts to trigger the agent actions.
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Action via plan following. In this strategy, the agent takes actions following its pregenerated plans.