reinforcement-learning-supply-chain

Reinforcement learning for supply chain optimization applies Q-learning, DQN, and policy gradient methods to sequential decision-making problems. This Skill trains agents to learn operational policies balancing holding and backorder costs under uncertainty.

You can train a DQN agent for inventory control by configuring parameters like mean demand and maximum inventory capacity. This Skill provides scripts to train agents that learn optimal ordering policies to balance holding and backorder costs.

You need PyTorch and NumPy to implement and train reinforcement learning agents for dynamic pricing and warehouse automation. These Python libraries provide the tensor operations and numerical computing support required for DQN and policy gradient algorithms.

Q-learning can solve supply chain problems involving continuous decision-making under uncertainty, such as dynamic pricing adjustments, warehouse robot navigation, and order fulfillment. It helps agents learn optimal actions based on inventory levels, demand, and market conditions.

You should use reinforcement learning over traditional optimization for supply chains when facing complex, sequential decision-making problems under uncertainty. RL excels at developing dynamic pricing and inventory policies where environments change based on inventory levels, demand, and market conditions.