MABIM

MABIM: Multi-Agent Benchmark for Inventory Management. Replenishment environment for OR and RL algorithms

Contents

Folder	Description
ReplenishmentEnv	Replenishment env source code.
ReplenishmentEnv/config	Config for building the env.
ReplenishmentEnv/data	Csv based data for skus, including sku_list, info and other dynamic data
ReplenishmentEnv/env	Kernel simulator for env
ReplenishmentEnv/utility	Utility simulator for env and wrapper
ReplenishmentEnv/wrapper	Wrapper for env
Baseline	Show case of replenishment env.

install MABIM

• Create new virtual environment (Optional) python -m venv myenv In Windows

myenv\Scripts\activate

In macOS and Linux:

source myenv/bin/activate

• Build and install MABIM

python setup.py install

Create a environment

• Create a build-in environment

from ReplenishmentEnv import make_env
make_env("{task_name}")

• Create a consumed environment
  • Prepare the data including SKU data and warehouse information.
  • Write config as the format of demo demo.yaml
  • Make the env by

from ReplenishmentEnv import make_env
make_env("{config_name}", config_dir="{config_dir}")

Run OR algorithm

• Install MABIM
• Install dependencies by pip install -r algo_requirements.txt
• Run OR algorithm by

import os
from Baseline.OR_algorithm.base_stock import BS_static, BS_dynamic
from Baseline.OR_algorithm.search_sS import sS_static, sS_hindsight
env_name = "sku200.single_store.standard"

# Base stock static mode
vis_path = os.path.join("output", env_name, "BS_static")
BS_static_sum_balance = sum(BS_static(env_name, vis_path))
print(env_name, "BS_static", BS_static_sum_balance)

# Base stock dynamic mode
vis_path = os.path.join("output", env_name, "BS_dynamic")
BS_static_sum_balance = sum(BS_dynamic(env_name, vis_path))
print(env_name, "BS_dynamic", BS_static_sum_balance)

# (s, S) static mode
vis_path = os.path.join("output", env_name, "sS_static")
sS_static_sum_balance = sum(sS_static(env_name, vis_path))
print(env_name, "BS_static", sS_static_sum_balance)

# (s, S) hindsight mode
vis_path = os.path.join("output", env_name, "sS_hindsight")
sS_hindsight_sum_balance = sum(sS_hindsight(env_name, vis_path))
print(env_name, "sS_hindsight", sS_hindsight_sum_balance)

Visualization policy will be in output folder.

Run MARL algorithm

The MARL training only tested in Linux. The training curve are available in wandb.

• Install MABIM
• Install dependencies by pip install -r algo_requirements.txt
• Specify the environment by modify the task_type field in replenishment.yaml
• IPPO training
  • Specify hyper parameter if needed in algorithm file, such as ippo.yaml
  • Run python main.py --config=ippo --env-config=replenishment
• QTRAN training
  • Specify hyper parameter if needed in algorithm file, such as qtran.yaml
  • Run python main.py --config=qtran --env-config=replenishment
• Get training curve in wandb
• If need visualization policy, set the visualize:True on algorithm file