Keyphrases
Control Policy
100%
Reinforcement Learning
100%
Learning Experience
100%
Lot Scheduling
100%
Deep Reinforcement Learning (deep RL)
100%
Scheduling Applications
100%
Recent Advances
50%
Single Machine
50%
Neural Network
50%
Production Control
50%
Reinforcement Learning Algorithm
50%
Proximal Policy Optimization Algorithm
50%
Parameterized Model
50%
Policy Approximation
50%
Stochastic Demand
50%
Base-stock Levels
50%
Optimal Parameter Value
50%
Average Cost Rate
50%
Value Function Approximation
50%
Discrete Production
50%
Control Value
50%
Base-stock Policy
50%
Policy Parameters
50%
Stochastic Economic Lot Scheduling Problem
50%
Proximal Policy Optimization
50%
INIS
applications
100%
policy
100%
learning
100%
control
42%
algorithms
42%
values
28%
optimization
28%
stochastic processes
28%
stocks
28%
economics
14%
levels
14%
production
14%
cost
14%
approximations
14%
demand
14%
manufacturing
14%
neural networks
14%
Computer Science
Learning Experiences
100%
Reinforcement Learning
100%
Deep Reinforcement Learning
66%
Parameter Value
33%
Scheduling Problem
33%
Optimization Policy
33%
Benchmarking
33%
Approximation (Algorithm)
33%
Neural Network
33%
Optimization Algorithm
33%
Industrial Management
33%
Psychology
Linear Model
100%
Neural Network
100%
Learning Algorithm
100%
Chemical Engineering
Reinforcement Learning
100%
Neural Network
20%
Engineering
Proximal Policy Optimization
40%