RL Supply Chain
Reinforcement Learning for Inventory Optimization
23%Cost Reduction
PPOAlgorithm
Real TimeDecisions
Problem
Traditional supply chain planning uses static rules that cannot adapt to demand volatility, leading to stockouts or excess inventory.
Solution
PPO agent trained in custom gymnasium environment that learns optimal reorder policies. Adapts to seasonal patterns and demand spikes.
Architecture
Demand Forecast → Custom Gym Environment → PPO Agent → FastAPI Endpoint → Inventory Decision → Reward Signal (minimize cost + stockout).
RL Supply Chain Optimization
Inventory Levels (PPO Agent)
OptimalReorder
Reorder Point
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PPO Reinforcement Learning
Proximal Policy Optimization agent trained in custom Gymnasium environment. Learns optimal reorder policies by minimizing cost while avoiding stockouts.
PPOAlgorithm
Real TimeDecisions
Stable Baselines3Azure MLGymnasium