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README.md

Status: Archive (code is provided as-is, no updates expected)

Large-Scale Study of Curiosity-Driven Learning

[Project Website] [Demo Video]

Yuri Burda*, Harri Edwards*, Deepak Pathak*,
Amos Storkey, Trevor Darrell, Alexei A. Efros
(* alphabetical ordering, equal contribution)

University of California, Berkeley
OpenAI
University of Edinburgh

This is a TensorFlow based implementation for our paper on large-scale study of curiosity-driven learning across 54 environments. Curiosity is a type of intrinsic reward function which uses prediction error as reward signal. In this paper, We perform the first large-scale study of purely curiosity-driven learning, i.e. without any extrinsic rewards, across 54 standard benchmark environments. We further investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (e.g. to novel game levels in Super Mario Bros.). If you find this work useful in your research, please cite:

@inproceedings{largeScaleCuriosity2018,
    Author = {Burda, Yuri and Edwards, Harri and
              Pathak, Deepak and Storkey, Amos and
              Darrell, Trevor and Efros, Alexei A.},
    Title = {Large-Scale Study of Curiosity-Driven Learning},
    Booktitle = {arXiv:1808.04355},
    Year = {2018}
}

Installation and Usage

The following command should train a pure exploration agent on Breakout with default experiment parameters.

python run.py

To use more than one gpu/machine, use MPI (e.g. mpiexec -n 8 python run.py should use 1024 parallel environments to collect experience instead of the default 128 on an 8 gpu machine).

Data for plots in paper

Data for Figure-2: contains raw game score data along with the plotting code to generate Figure-2 in the paper.

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Code for the paper "Large-Scale Study of Curiosity-Driven Learning"

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