Multi HRI

Set up guide

1. Follow setup instrcution for overcooked_ai from here
2. Clone this repository: git clone git@github.com:HIRO-group/multiHRI.git
3. Activate conda env: conda activate mhri
4. cd into the repo and run: pip install -e .. If step 3 did not work, try commenting the liblsl in setup.py and install liblsl either from conda or pip.

General Structure

• oai_agents: Main directory
  • /agents: Contains code related to creating and training agents:
    • /il.py, /rl.py, and /hrl.py contain code for training an imitation learning, reinforcement learning, and hierarchical reinforcement learning respectively.
    • /base_agent.py contains the abstract base class for the agent and trainer objects, including default save/load methods and interfacing methods
  • /common: Generally used functions and objects
    • /arguments.py: Code to parse cmd line args (and the defaults for many values)
    • /networks.py: All nn modules
    • /overcooked_dataset.py: Pytorch dataset wrapper for human play data used for il
    • /state_encodings.py: Code used to manipulate the state into agent obs
    • /subtasks.py: All code related to defining, identifying, and using subtasks
  • /gym_environments.py: Gym environments wrappers for main overcooked logic
    • /base_overcooked_env.py: Wrap main game logic with some additional functionality. Used to train rl agents.
    • /manager_env.py: Environment used to train manager component of hrl. Changes action space among other small things
    • /worker_env.py: Environment used to train worker component of hrl. Changes reward and start state among other small things
• scripts: Generally useful scripts
  • /colosseum.py: Have agents play with other agents for evaluation
  • /run_overcooked_game.py: Visualize agent-agent or human-agent team playing with each other
  • /train_agents.py: Functions to train different kinds of agents

Training agents

Refer to scripts/train_agents.py for examples on how to train different agent trainings. If you've installed the package as above, you can run the script using:
python scripts/train_agents.py

Citation

If you use this repository in any way, please cite:

{@inproceedings{10.5555/3545946.3598926,
author = {Aroca-Ouellette, St\'{e}phane and Aroca-Ouellette, Miguel and Biswas, Upasana and Kann, Katharina and Roncone, Alessandro},
title = {Hierarchical Reinforcement Learning for Ad Hoc Teaming},
year = {2023},
isbn = {9781450394321},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
address = {Richland, SC},
abstract = {When performing collaborative tasks with new unknown teammates, humans are particularly adept at adapting to their collaborator and converging toward an aligned strategy. However, state of the art autonomous agents still do not have this capability. We propose that a critical reason for this disconnect is that there is an inherent hierarchical structure to human behavior that current agents lack. In this paper, we explore the use of hierarchical reinforcement learning to train an agent that can navigate the complexities of ad hoc teaming at the same level of abstraction as humans. Our results demonstrate that when paired with humans, our Hierarchical Ad Hoc Agent (HAHA) outperforms all baselines on both the team's objective performance and the human's perception of the agent.},
booktitle = {Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems},
pages = {2337–2339},
numpages = {3},
keywords = {ad hoc teaming, hierarchical reinforcement learning, human agent collaboration, mutual adaptation, zero-shot coordination},
location = {London, United Kingdom},
series = {AAMAS '23}
}