Thought Cloning: Learning to Think while Acting by Imitating Human Thinking [project page] [arxiv]

@article{hu2023ThoughtCloning,
  title={{Thought Cloning}: Learning to Think while Acting by Imitating Human Thinking},
  author={Shengran Hu and Jeff Clune},
  year={2023},
  journal={arXiv preprint arXiv:2306.00323},
}

Overview

This repository provides the official implementation for Thought Cloning: Learning to Think while Acting by Imitating Human Thinking. Thought Cloning (TC) is a novel imitation learning framework that enhances agent capability, AI Safety, and Interpretability by training agents to think like humans. This repository implements TC on a simulated partially observable 2D gridworld domain BabyAI with a synthetic human thought dataset. Also check the introduction tweet thread.

Installation

Creating Environment

Create environment with python-venv or conda. Example with python-venv is shown here:

python3 -m venv thoughtcloning
source thoughtcloning/bin/activate 

Key Requirements

• Python >= 3.6
• PyTorch >= 1.7.1
• OpenAI Gym == 0.9.6
• NumPy == 1.19.5
• gym-minigrid == 1.0.0
• blosc

This repository has been tested with Python 3.9.10 and PyTorch 1.7.1+cu110. The code may not be compatible with higher version of gym, numpy or gym-minigrid.

Installation

1. Install PyTorch 1.7.1+ following PyTorch official tutorial.
2. Clone this repository and install the other dependencies with pip3

git clone https://github.com/ShengranHu/Thought-Cloning.git
cd Thought-Cloning
pip3 install --upgrade pip
pip3 install --editable .

Note: run pip3 install --editable . again if the project directory is changed afterward.

Configuring Path

Change the output directory in storage_dir in /babyai/utils/__init__.py. Set path-to-thought-cloningto the path of your Thought Cloning project.

Reproduce the results in the paper

The synthetic human thought dataset on BossLevel, trained TC model weights, and collected environments to test out-of-distribution performance are available in Google Drive.

Reproduce Synthetic Thought Dataset

To reproduce a synthetic thought dataset, you can use the scripts/make_agent_demos.py script with the following command:

scripts/make_agent_demos.py --episodes <NUM_OF_EPISODES> --env <ENV_NAME> --noise-rate 0.01

Thought Cloning Training

To reproduce the main performance results presented in Fig. 3 of our paper, use the following command:

scripts/train_tc.py --env BabyAI-BossLevel-v0 --demos <DEMO_NAME> --memory-dim=2048 --recurrence=80 --batch-size=180 --instr-arch=attgru --instr-dim=256 --val-interval 2 --log-interval 2 --lr 5e-4 --epochs 160 --epoch-length 51200 --seed 0 --val-seed 2023 --model <NAME_OF_MODEL> --sg-coef 2 --warm-start --stop-tf 10

For each experiment, you can change the group_name argument to group log and model output into a specific folder.

Zero-shot Evaluation on Out-of-Distribution Environments

To reproduce the zero-shot evaluation results presented in Fig. 4(a) of our paper, use the following command:

scripts/evaluate_levels.py --env BabyAI-BossLevel-v0 --model <NAME_OF_MODEL> --testing-levels-path <PATH_TO_TESTING_LEVELS_PICKLE>

Related links

Our implementation is based on BabyAI 1.1 (domain and Imitation Learning baseline), dan-visdial (Upper-level Component Transformer encoder), and visdial-rl (Upper-level Component RNN decoder).