DTRGym 0.1.0

A Collection of Reinforcement Learning Environments for Dynamic Treatment Regime Simulation.

DTRGym: Reinforcement learning Environments for Dynamic Treatment Regimes

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📝 Table of Contents

• About
• Getting Started
• Module Description
• Usage
• Reference
• Special Thanks
• Acknowledge

🧐 About

DTR-Gym is a benchmarking platform with four unique simulation environments aimed at improving treatments in areas including cancer chemotherapy, tumor growth, diabetes, and sepsis therapy.

The design of DTR-Gym is committed to replicate the intricacies of real clinical scenarios, thereby providing a robust framework for exploring and evaluating reinforcement learning algorithms.

🏁 Getting Started

These instructions will get you a copy of the project up and running on your local machine.

Prerequisites

• Python 3.10: The project is developed using Python 3.10. It is recommended to use the same version to avoid compatibility issues.

Installing

1. Clone the repository

git clone [e-mail]:GilesLuo/SimMedEnv.git

2. Install the required packages

cd SimMedEnv
pip install -r requirements.txt

3. Test the installation

python test_installation.py

Initialise the Environment

You can run the example by:

import gymnasium as gym
import DTRGym  # this line is necessary!

env = gym.make('AhnChemoEnv-discrete', n_act=11)
print(env.action_space.n)
print(env.observation_space.shape)

🎈 Module Description

Simulation Environments

There are four simulation environments in the DTRGym. Each environment simulates a specific disease and treatment.

Environment	Disease	Treatment	Dynamics	Action Space
AhnChemoEnv	Cancer	Chemotherapy	ODE	Cont./Disc.
GhaffariCancerEnv	Cancer	Chemotherapy & Radiotherapy	ODE	Cont./Disc.
OberstSepsisEnv	Sepsis	Antibiotics, Mechanical Ventilation, Vasopressors	SCM	Disc.
SimGlucoseEnv	Type-1 Diabetes	Insulin Administration	ODE	Cont./Disc.

Environment Settings

There are five default settings for each environment. The settings are designed to simulate different scenarios in the real world. The settings include:

Setting	Description
1	No PK/PD variation, no observation noise, no missing values.
2	PK/PD variation, no observation noise, no missing values.
3	PK/PD variation, observation noise (medium), no missing values.
4	PK/PD variation, observation noise (large), no missing values.
5	PK/PD variation, observation noise (large), missing values.

For different environments, the variations are defined as follows:

Environment	PK/PD Variation	Observation Noise (Medium)	Observation Noise (Large)	Missing Values
AhnChemoEnv	10%	20%	50%	50%
GhaffariCancerEnv	10%	10%	20%	50%
OberstSepsisEnv	10%	20%	50%	50%
SimGlucoseEnv	Parameters of different patients	Use data from simulated glucose monitor.	Further randomize food intake times.	50%

🔧 Usage

Use Default Environment Configuration

DTR-Gym provides default environment configuration to simulate the real-world clinical scenarios. For example, if you want to use the setting 1, you can initialise the environment by

import gymnasium as gym
import DTRGym

env = gym.make("AhnChemoEnv-continuous-setting1")

Customize Maximum Timestep

You can set the maximum available timestep for the environment by passing value to max_t. Here's an example:

import gymnasium as gym
import DTRGym

env = gym.make("AhnChemoEnv-continuous", max_t=50)
print(env.max_t)

Choose Action Space

When creating the environment, you can choose from a discrete action space version or a continuous action space version. For all the environment except "TangSepsisEnv-discrete", which only has the discrete actions space version, you can choose different action space by pass id. The environment with same id prefix are only different on the type of action space. They have the same observation space, same disease dynamics, and the same reward function. So feel free to choose the environment according to your RL policy.

Here's an example:

import DTRGym

continuous_env = gym.make("AhnChemoEnv-continuous")
discrete_env = gym.make("AhnChemoEnv-discrete")

print(continuous_env.env_info["action_type"])
print(discrete_env.env_info['action_type'])
print(continuous_env.observation_space.sample() in discrete_env.observation_space)

Customize Action Number (for Discrete Action Space Env)

You can also set the the number of action you want the environment to have by using the n_act. This is only effective for the discrete version. Here is an example:

import DTRGym

env = gym.make("AhnChemoEnv-discrete", n_act=5)
print(env.n_act)

Reference

If you use the DTR-Gym in your research, please cite the following paper:

To be updated

✍️ Sepcial Thanks

Special thanks to the following contributors that make the DTR-Gym possible:

• @Mingcheng Zhu - who developed DTRGym and produced extensive DTRBench experiments.
• To be continued

🎉 Acknowledgement

• Gymnasium
• Simglucose
• gumbel-max-scm