Proximal Policy Optimization (PPO) agent incorporates both predictive forecasts and explanatory signals into its state …

fulfillment and lateral inventory rebalancing are controlled daily. Policies are learned using Proximal Policy Optimization (PPO …

and traffic. Our approach integrates graph neural networks to encode spatial problem structure and proximal policy … optimization to train robust policies under both demand and traffic uncertainty. Experiments on synthetic grid-based routing …

deploy a Proximal Policy Optimization (PPO) agent that incorporates deep learning outputs as part of its observation space …

-learning environment for portfolio reallocation, we train actor-critic agents (Proximal Policy Optimization (PPO) and Advantage Actor …

policy optimization and consists of a decoder and encoder, allowing for various-sized system state descriptions. A simulation … scheduling in line-less mobile assembly systems. The proposed multi agent deep reinforcement learning algorithm uses proximal …