| __pycache__ | ||
| baselines@ea25b9e8b2 | ||
| dmc2gym | ||
| local_dm_control_suite | ||
| ROMS | ||
| __init__.py | ||
| cnn_policy.py | ||
| cppo_agent.py | ||
| dynamic_bottleneck.py | ||
| environment.yml | ||
| mpi_utils.py | ||
| para.json | ||
| README.md | ||
| recorder.py | ||
| rollouts.py | ||
| run.py | ||
| utils.py | ||
| vec_env.py | ||
| wrappers.py | ||
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".