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This notebook showcases how to implement a multi-agent simulation without a fixed schedule for who speaks when. Instead the agents decide for themselves who speaks. We can implement this by having each agent bid to speak. Whichever agent's bid is the highest gets to speak. We will show how to do this in the example below that showcases a fictitious presidential debate.
19 lines
1.6 KiB
Markdown
19 lines
1.6 KiB
Markdown
# Agent Simulations
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Agent simulations involve interacting one of more agents with eachother.
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Agent simulations generally involve two main components:
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- Long Term Memory
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- Simulation Environment
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Specific implementations of agent simulations (or parts of agent simulations) include
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## Simulations with Two Agents
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- [CAMEL](agent_simulations/camel_role_playing.ipynb): an implementation of the CAMEL (Communicative Agents for “Mind” Exploration of Large Scale Language Model Society) paper, where two agents communicate with each other.
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- [Two Player D&D](agent_simulations/two_player_dnd.ipynb): an example of how to use a generic simulator for two agents to implement a variant of the popular Dungeons & Dragons role playing game.
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## Simulations with Multiple Agents
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- [Multi-Player D&D](agent_simulations/multi_player_dnd.ipynb): an example of how to use a generic dialogue simulator for multiple dialogue agents with a custom speaker-ordering, illustrated with a variant of the popular Dungeons & Dragons role playing game.
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- [Decentralized Speaker Selection](agent_simulations/multiagent_bidding.ipynb): an example of how to implement a multi-agent dialogue without a fixed schedule for who speaks when. Instead the agents decide for themselves who speaks by outputting bids to speak. This example shows how to do this in the context of a fictitious presidential debate.
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- [Generative Agents](agent_simulations/characters.ipynb): This notebook implements a generative agent based on the paper [Generative Agents: Interactive Simulacra of Human Behavior](https://arxiv.org/abs/2304.03442) by Park, et. al.
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