Dynamic Bottleneck for Robust Self-Supervised Exploration

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Overview

Dynamic Bottleneck

Introduction

This is a TensorFlow based implementation for our paper on

"Dynamic Bottleneck for Robust Self-Supervised Exploration". NeurIPS 2021

Prerequisites

python3.6 or 3.7, tensorflow-gpu 1.x, tensorflow-probability, openAI baselines, openAI Gym

Installation and Usage

Atari games

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

python run.py --env BreakoutNoFrameskip-v4

Atari games with Random-Box noise

The following command should train a pure exploration agent on "Breakout" with randomBox noise.

python run.py --env BreakoutNoFrameskip-v4 --randomBoxNoise

Atari games with Gaussian noise

The following command should train a pure exploration agent on "Breakout" with Gaussian noise.

python run.py --env BreakoutNoFrameskip-v4 --pixelNoise

Atari games with sticky actions

The following command should train a pure exploration agent on "sticky Breakout" with a probability of 0.25

python run.py --env BreakoutNoFrameskip-v4 --stickyAtari

Baselines

  • ICM: We use the official code of "Curiosity-driven Exploration by Self-supervised Prediction, ICML 2017" and "Large-Scale Study of Curiosity-Driven Learning, ICLR 2019".
  • Disagreement: We use the official code of "Self-Supervised Exploration via Disagreement, ICML 2019".
  • CB: We use the official code of "Curiosity-Bottleneck: Exploration by Distilling Task-Specific Novelty, ICML 2019".
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