https://github.com/ml-jku/reactive-exploration
Code for the paper "Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning"
https://github.com/ml-jku/reactive-exploration
Last synced: about 2 months ago
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Code for the paper "Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning"
- Host: GitHub
- URL: https://github.com/ml-jku/reactive-exploration
- Owner: ml-jku
- License: mit
- Created: 2022-07-04T11:23:00.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2022-07-04T12:05:41.000Z (almost 3 years ago)
- Last Synced: 2025-04-05T13:23:34.662Z (2 months ago)
- Language: Python
- Homepage:
- Size: 372 KB
- Stars: 14
- Watchers: 3
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning
Christian Steinparz**2**, Thomas Schmied**1**, Fabian Paischer**1**, Marius-Constantin Dinu**1,3**, Vihang Patil**1**, Angela Bitto-Nemling**1,4**, Hamid Eghbal-Zadeh**1**, Sepp Hochreiter**1,4**
**1**ELLIS Unit Linz and LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria\
**2**Visual Data Science Lab, Institute of Compute Graphics, Johannes Kepler University Linz, Austria\
**3**Dynatrace Research, Linz, Austria\
**4**Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria
This repository contains the source code for our paper **"Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning"** accepted at [CoLLAs 2022](https://lifelong-ml.cc/).
[//]: # (---)
[//]: # ()
[//]: # (**This is the repository for the paper:)
[//]: # ([X](https://arxiv.org/abs/2205.12258).**)
[//]: # ()
[//]: # (**Detailed blog post on this paper at [this link](https://ml-jku.github.io/blog/2022/helm/).**)
[//]: # ()
[//]: # (---)
## Installation
[](https://www.python.org/downloads/release/python-390/)
[](https://pytorch.org/get-started/previous-versions/)
First, clone the repository and install the conda environment from the repository root (using either linux or windows config file):
```
conda env create -f environment_linux.yaml
conda activate reactive_exploration
```
Then follow the Jelly-Bean-World [installation instructions](https://github.com/eaplatanios/jelly-bean-world#installation-instructions). We use the following version:
```
git clone https://github.com/eaplatanios/jelly-bean-world.git
cd jelly-bean-world
git checkout 9bb16780e72d9d871384f9bcefd3b4e029a7b0ef
git submodule update --init --recursive
cd api/python
python setup.py install
```
From the project root directory install and update the submodule for ICM+PPO:
```
git submodule update --init --recursive
```
## Running experiments
This codebase relies on [Hydra](https://github.com/facebookresearch/hydra), which configures experiments via `.yaml` files.
Hydra automatically creates the log folder structure for a given run, as specified in the respective `config.yaml` file.
### Running single experiments
By default, Hydra uses the configuration in `configs/config.yaml`.
This config file defines how Hydra generates the directory structures for executed experiments under the block `hydra`.
The `config.yaml` contains the default parameters. The file references the respective default parameter files under the block
`defaults`.
By default, `main.py` trains a PPO+ICM agent on the Colour-Swap task from the paper by referencing the environment configuration `configs/env_params/colour_swap.yaml` and agent configuration `configs/agent_params/icm.yaml`
To execute this configuration, run:
```
python main.py
```
For other configurations regarding agents and environments reference the files in `configs/`. For instance, to execute PPO+ICM on the Rotation task, run:
```
python main.py env_params=rotator
```
Experiment logs are synced to [wandb](https://github.com/wandb/client). To execute experiments without wandb-logging, run:
```
python main.py use_wandb=False
```
### Running multiple experiments
All hyperparameters specified in the `.yaml` configuration files can be manipulated from the commandline.
For example, to execute ICM and RND on the Colour-Swap task and Rotation task (`configs/env_params/rotator.yaml`) using 5 seeds, run:
```
python -m main.py agent_params=icm,rnd env_params=colour_swap,rotator seed=1,2,3,4,5
```
## Citation
This paper has been accepted to the Conference on Lifelong Learning Agents (CoLLAs) 2022. While the conference proceedings do not yet exist, we recommend the following citation:
```bib
@misc{steinparz2022reactiveexp,
title={Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning},
author={Steinparz, Christian and Schmied, Thomas and Paischer, Fabian and Dinu, Marius-Constantin and Patil, Vihang and
Bitto-Nemling, Angela and Eghbal-zadeh, Hamid and Hochreiter, Sepp},
journal={arXiv preprint, accepted to Conference on Lifelong Learning Agents 2022},
year={2022},
eprint={X}
}
```