MazeRL is an application oriented Deep Reinforcement Learning (RL) framework, addressing real-world decision problems. Our vision is to cover the complete development life cycle of RL applications ranging from simulation engineering up to agent development, training and deployment.
Applied Reinforcement Learning at the example of stock replenishment for an industrial group.
Our CTO provides a walkthrough on how to apply reinforcement learning on stock replenishment and shows the significant cost savings potential.
Reinforcement learning is often described as the next big step for AI with the ability to surpass human capabilities in optimization & decision making. However, when it comes to practical applications, up until recently there have been very few use cases made public.