Deep active inference agents using Monte-Carlo methods

This source code release accompanies the manuscript:

Z. Fountas, N. Sajid, P. A.M. Mediano and K. Friston "Deep active inference agents using Monte-Carlo methods", Advances in Neural Information Processing Systems 33 (NeurIPS 2020).

If you use this model or the dynamic dSprites environment in your work, please cite our paper.

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Description

For a quick overview see this video. In this work, we propose the deep neural architecture illustrated below, which can be used to train scaled-up active inference agents for continuous complex environments based on amortized inference, M-C tree search, M-C dropouts and top-down transition precision, that encourages disentangled latent representations.

We test this architecture on two tasks from the Animal-AI Olympics and a new simple object-sorting task based on DeepMind's dSprites dataset.

Demo behavior

Agent trained in the Dynamic dSprites environment	Agent trained in the Animal-AI environment

Requirements

• Programming language: Python 3
• Libraries: tensorflow >= 2.0.0, numpy, matplotlib, scipy, opencv-python
• dSprites dataset.

Instructions

Installation

• Initially, make sure the required libraries are installed in your computer. Open a terminal and type

pip install -r requirements.txt

• Then, clone this repository, navigate to the project directory and download the dSrpites dataset by typing

wget https://github.com/deepmind/dsprites-dataset/raw/master/dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz

or by manually visiting the above URL.

Training

• To train an active inference agent to solve the dynamic dSprites task, type

python train.py

This script will automatically generate checkpoints with the optimized parameters of the agent and store this checkpoints to a different sub-folder every 25 training iterations. The default folder that will contain all sub-folders is figs_final_model_0.01_30_1.0_50_10_5. The script will also generate a number of performance figures, also stored in the same folder. You can stop the process at any point by pressing Ctr+c.

Testing

• Finally, once training has been completed, the performance of the newly-trained agent can be demonstrated in real-time by typing

python test_demo.py -n figs_final_model_0.01_30_1.0_50_10_5/checkpoints/ -m

This command will open a graphical interface which can be controlled by a number of keyboard shortcuts. In particular, press:

• q or esc to exit the simulation at any point.
• 1 to enable the MCTS-based full-scale active inference agent (enable by default).
• 2 to enable the active inference agent that minimizes expected free energy calculated only for a single time-step into the future.
• 3 to make the agent being controlled entirely by the habitual network (see manuscript for explanation)
• 4 to activate manual mode where the agents are disabled and the environment can be manipulated by the user. Use the keys w, s, a or d to move the current object up, down, left or right respectively.
• 5 to enable an agent that minimizes the terms a and b of equation 8 in the manuscript.
• 6 to enable an agent that minimizes only the term a of the same equation (reward-seeking agent).
• m to toggle the use of sampling in calculating future transitions.

Bibtex

@inproceedings{fountas2020daimc,
author = {Fountas, Zafeirios and Sajid, Noor and Mediano, Pedro and Friston, Karl},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
pages = {11662--11675},
publisher = {Curran Associates, Inc.},
title = {Deep active inference agents using Monte-Carlo methods},
url = {https://proceedings.neurips.cc/paper/2020/file/865dfbde8a344b44095495f3591f7407-Paper.pdf},
volume = {33},
year = {2020}
}