LAVA-LAB/ATM

Repository containing code for ICAPS-paper "Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuring"

ATM Repository

Repository containing code for ATM-Q (referred to here as BAM-QMDP), and gathered data, as used in the paper:

Merlijn Krale, Thiago D. Simão, and Nils Jansen
Act-Then-Measure: Reinforcement Learning for Partially Observable Environments with Active Measuring
In ICAPS, 2023.

Contents

This repository contains the following files:

Code:

• BAM_QMDP.py : The BAM-QMDP (a.k.a. (Dyna-)ATMQ) agent as a python class.
• Plot_Data.ipynb : Code for plotting data.
• Run.py : Code for automatically running agents on environments & recording their data.
• RunAll.sh : Bashfile for running all experiments at once.

Folders:

• AM_Gyms : Contains Gym environments used for testing, and wrapper class to make generic OpenAI envs into ACNO-MDP envs.
• Data : Contains gahtered date for BNAIC and ICAPS-paper (including analysed data & standard plots).
• Final_Plots : Contains compiled plots.
• Baselines : Contains code for all baseline algorithms used in the paper or in the testing phase.

Getting started

After cloning this repository:

1. create a virtualenv and activate it

cd ATM/
python3 -m venv .venv
source .venv/bin/activate

2. install the dependencies

pip install -r requirements.txt

How to run

All algorithms can be run using the Run.py file from command line. Running 'python Run.py -h' gives an overview of the functionaliality.

As an example, starting a run looks something like:

python Run.py -algo BAM_QMDP -env Lake -env_gen standard -env_size 8 -env_var semi-slippery -nmbr_eps 2500

This command runs the BAM-QMDP algorithm on the 8x8 semi-slippery lake environment for 2500 episodes (1 run), then it saves the results in the 'Data' folder. For convenience, all experiments used in the paper are combined in a bashfile, which can be called using './RunAll.sh'.