![]() ![]() This helps match production capacity to demand. When the nearest factory to a distribution center is about to reach capacity the RL agent places orders at factories further away. The main difference here is that RL policy learned to dynamically assign orders. The reason RL beat the other heuristics by so much difference is because it could account for the fact that sometimes factories get overloaded by demand. The method produced a waiting time more than four times shorter than the Nearest Agent heuristic. The results obtained were extremely good. If the waiting time increases the function becomes ever more negative, so the RL agent knows it is performing poorly.įigure 1: AvgWaitingTime (blue) and AvgDistanceTraveled (green) while training. This means we only tried to minimize the waiting time. Reward = before.avgWaitingTime – after.AvgWaitingTime The RL will be trained to try to maximize this function. The reward function is the way of telling the RL agent if it is performing well or not. If no order is generated, the action is ignored for that distribution center. As the 15 distribution centers create orders, the RL agent decides which of the 3 manufacturing centers should fulfill each one. In this case, the action space is a vector of size 15x3. The action space is the range of actions our RL agent can make decisions for. ![]() 0 if no order was placed for a distribution center Order Amounts: The number of items ordered.Free Vehicles: The number of available vehicles each manufacturing center has.Starting Vehicles: The number of vehicles each manufacturing center has.Stock Info: The current stock of each manufacturing center.Is important to give information that will be available in the real environment since the final goal is for it to work there.įor our model we choose to give to the agent the following data: It will only investigate these variables when deciding which action to take. These elements are: the observation space, the action space, and the reward function. There are three key elements to define when making a neural net. Furthermore, a simulated environment can be run many times under different conditions, allowing RL algorithms to train on thousands of simulated years of possibilities. In this case, there cannot be any better training ground than a simulated environment because the associated costs are minimal in comparison to real life testing. This pairing is critical for policy training because learning algorithms need time to learn which actions work best in different situations – time that would be difficult to provide outside of a computing environment. Pathmind is combining the newest RL algorithms with AnyLogic simulation modeling. To achieve its goal, Accenture partnered with San Francisco based AI company Pathmind. Read on, learn about the model and how it uses reinforcement learning, and then follow the tutorial. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |