Hypothetical Minds is an autonomous LLM-based agent for diverse multi-agent settings, integrating a Theory of Mind module that scaffolds the high-level planning process by generating, evaluating, and refining hypotheses about other agents’ strategies in natural language.
To learn more:
Install MeltingPot in editable mode from https://github.com/locross93/meltingpot, then install this repo
pip install -e .
Set up your API key with an environment variable:
export OPENAI_API_KEY=your_api_key_here
To run an episode of Hypothetical Minds, use main.py as in the following example with "Running With Scissors Repeated":
python main.py --substrate rws --scenario_num 0 --agent_type hm --llm_type gpt4To loop through every scenario in a substrate, use run_scenarios.py as in the following example running the Reflexion baseline on "Collaborative Cooking Asymmetric":
python run_scenarios.py --agent reflexion --substrate cc --num_seeds 5| Alias | Description |
|---|---|
cc |
collaborative_cooking__asymmetric - In this environment, two players operate on opposite sides of a divided kitchen, where they must collaborate to efficiently prepare tomato soup, with each player specializing in tasks based on their proximity to resources. |
rws |
running_with_scissors_in_the_matrix__repeated - A zero-sum competitive environment where two players navigate a map collecting resources represented as yellow (rock), purple (paper), or blue (scissors). Players can "zap" each other to initiate a rock-paper-scissors style interaction based on their collected resources, resulting in one player receiving a positive reward and the other a corresponding negative reward. |
rws_arena |
running_with_scissors_in_the_matrix__arena - An eight player extension of RWS, where the focal agent controls one player against a background population of 7 strategies. |
pd |
prisoners_dilemma_in_the_matrix__repeated - Agents navigate a map similar to RWS, where they collect resources that correspond to cooperation or defection, reflecting the choices in the iterated prisoner’s dilemma game. |
To run open source models like LLaMA 3, you need to set up vllm first.
- Install vllm:
pip install vllm
- Start the vllm server:
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-70B-Instruct --port 8000 --tensor-parallel-size 4 --seed 1234
- Run the agent with LLaMA 3:
python main.py --substrate rws --scenario_num 0 --agent_type hm --llm_type llama3