Applying RLLib to Real-Life Industrial Use Cases | 

Agenda - Pathmind and Ray - Industrial Engineering workflow and optimization tactics. - Heuristics vs Optimizers vs RL - When and why you should try Reinforcement Learning - Simulation + RL Demo 

| Pathmind and Ray - Bridge between real-life industrial processes and RL - We use RLLib out-of-box (PPO, Population-Based Training) Digital Twin 

| Industrial Engineering Workflow Simulation/Digital Twin (Observations) Decisions (Action) Outcomes (Reward) Heuristics Static rules defined by a domain expert (if this, then that). Optimizers RL Automatically finds the best parameters of a model, with respect to certain constraints. 

● Heuristics ○ Pros ■ Easy to understand and implement. ■ Factory manager knows exactly what is going on. ○ Cons ■ Not scalable. Can grow to hundreds of rules. ■ Inflexible and doesn’t react to change. | Heuristics vs Optimizers vs RL 

● Optimizers ○ Pros ■ Relatively easy to set up ■ Not a black box ○ Cons ■ Clunky and slow in complex scenarios. ■ Static in nature. Have to re-run optimizer whenever things change. | Heuristics vs Optimizers vs RL 

● Reinforcement Learning ○ Pros ■ Good with high variability ■ Handles large state spaces ■ Can navigate multiple contradictory objectives ○ Cons ■ Needs a data scientist ■ Black box, hard to explain why a policy made a decision | When and Why Reinforcement Learning 

| Use Cases 

Simulation Demo 

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