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Drop-Bottleneck (DB)


This is the code for our paper,

In the paper, we propose a novel information bottleneck (IB) method named Drop-Bottleneck, which discretely drops features that are irrelevant to the target variable. Drop-Bottleneck not only enjoys a simple and tractable compression objective but also additionally provides a deterministic compressed representation of the input variable, which is useful for inference tasks that require consistent representation. Moreover, it can jointly learn a feature extractor and select features considering each feature dimension’s relevance to the target task, which is unattainable by most neural network-based IB methods. We propose an exploration method based on Drop-Bottleneck for reinforcement learning tasks. In a multitude of noisy and reward sparse maze navigation tasks in VizDoom and DMLab, our exploration method achieves state-of-the-art performance. As a new IB framework, we demonstrate that Drop-Bottleneck outperforms Variational Information Bottleneck (VIB) (Alemi et al., 2017) in multiple aspects including adversarial robustness and dimensionality reduction. This repository provides the implementation for the exploration method with Drop-Bottleneck and the DMLab navigation tasks.

Citing the paper

If you find our work or this code useful in your research, please cite

    title={Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust Exploration},
    author={Kim, Jaekyeom and Kim, Minjung and Woo, Dongyeon and Kim, Gunhee},
    booktitle={International Conference on Learning Representations (ICLR)},


This codebase was tested on environments with the following components:

  • Ubuntu 16.04 machine
  • sudo privileges (for installing dependencies)
  • CUDA-compatible GPUs
  • Anaconda

Environment setup

  1. Install dependencies for the DMLab pip package following the instructions
  2. (In the main directory) create conda env and activate it by running:
    conda env create -f environment.yml
    conda activate db-expl
    Make sure that the environment variable CONDA_PREFIX is properly set.
  3. Clone DMLab and build and install DMLab with essential modifications:
    git clone
    cd lab
    git checkout 7b851dcbf6171fa184bf8a25bf2c87fe6d3f5380
    git apply ../third_party/dmlab/dmlab_min_goal_distance.patch
    git apply ../third_party/dmlab/dmlab_conda.patch
    bash ./ will try to install some required packages by running sudo commands.


In order to obtain Table 1 from the paper, the following training commands need to be run:

Command Reward condition Noise setting Average Reward Sum (Test)
python scripts/ --scenario sparse --noise_type image_action Sparse Image Action 30.4
python scripts/ --scenario sparse --noise_type noise Sparse Noise 32.7
python scripts/ --scenario sparse --noise_type noise_action Sparse Noise Action 30.6
python scripts/ --scenario verysparse --noise_type image_action Very Sparse Image Action 28.8
python scripts/ --scenario verysparse --noise_type noise Very Sparse Noise 29.1
python scripts/ --scenario verysparse --noise_type noise_action Very Sparse Noise Action 26.9

Note that the results in Table 1 are the test reward sums averaged over 30 runs for each setting and the actual images used for "Image Action" tasks are not included in this codebase since we do not hold the right to distribute them.


  • Each training command creates an experiment directory under exp directory.
  • Experiment directories contain reward_test.csv files, which list episode reward sums in test environments at each evaluation step.
  • From the reward_test.csv files, final test reward sums can be obtained by taking the values at step 20M (20044800). Thus, gathering the results can be easily done with shell commands such as
    grep "20044800," exp/*/reward_test.csv


This source code is based on the official implementation for Episodic Curiosity Through Reachability.


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