Beer Game Environment is a Python-based OpenAI Gym environment simulating the classic Beer Game supply chain with four roles. It lets agents observe inventory levels and place orders, modeling demand fluctuations, lead times, and cost calculations. Users can train and evaluate RL agents via the standard Gym API to minimize inventory holding and backorder costs across retailer, wholesaler, distributor, and manufacturer nodes.
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Beer Game Environment
Beer Game Environment is a Python-based OpenAI Gym environment simulating the classic Beer Game supply chain with four roles. It lets agents observe inventory levels and place orders, modeling demand fluctuations, lead times, and cost calculations. Users can train and evaluate RL agents via the standard Gym API to minimize inventory holding and backorder costs across retailer, wholesaler, distributor, and manufacturer nodes.
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What is Beer Game Environment?
The Beer Game Environment provides a discrete-time simulation of a four-stage beer supply chain—retailer, wholesaler, distributor, and manufacturer—exposing an OpenAI Gym interface. Agents receive observations including on-hand inventory, pipeline stock, and incoming orders, then output order quantities. The environment computes per-step costs for inventory holding and backorders, and supports customizable demand distributions and lead times. It integrates seamlessly with popular RL libraries like Stable Baselines3, enabling researchers and educators to benchmark and train algorithms on supply chain optimization tasks.
Who will use Beer Game Environment?
- Reinforcement learning researchers
- Supply chain and operations management professionals
- AI and data science educators
- Students studying supply chain optimization
How to use the Beer Game Environment?
- Step1: Install the package via pip: pip install beer-game-env
- Step2: Import the environment: from beer_game_env import BeerGameEnv
- Step3: Instantiate the environment: env = BeerGameEnv()
- Step4: Use standard Gym loop: obs = env.reset(), action = agent.predict(obs), obs, reward, done, info = env.step(action)
- Step5: Train or evaluate agents using any Gym-compatible RL framework
Platform
- Linux
- Mac
- Windows
Beer Game Environment's Core Features & Benefits
The Core Features
- OpenAI Gym compliant environment
- Simulates retailer, wholesaler, distributor, manufacturer roles
- Customizable demand distributions and lead times
- Per-step cost calculation for inventory and backorders
- Seamless integration with RL libraries
The Benefits
- Standard Gym API compatibility
- Easy integration with popular RL frameworks
- Detailed and realistic supply chain simulation
- Facilitates benchmarking of agent performance
- Ideal for research and educational purposes
Beer Game Environment's Main Use Cases & Applications
- Training reinforcement learning agents for supply chain optimization
- Benchmarking inventory management and ordering algorithms
- Teaching supply chain dynamics in academic courses
- Research on decentralized decision-making under demand uncertainty
FAQs of Beer Game Environment
Beer Game Environment Company Information
Beer Game Environment Reviews
5/5
Beer Game Environment's Main Competitors and alternatives?
supply-chain-gym
gym-supplychain
SimPy supply chain simulator
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