REINFORCEMENT LEARNING SYSTEMS AND METHODS FOR INVENTORY CONTROL AND OPTIMIZATION
Number of patents in Portfolio can not be more than 2000
United States of America
Stats
-
N/A
Issued Date -
Dec 23, 2021
app pub date -
Oct 21, 2019
filing date -
Oct 21, 2019
priority date (Note) -
Published
status (Latency Note)
Importance
Abstract
Methods of reinforcement learning for a resource management agent. Responsive to generated actions, corresponding observations are received. Each observation comprises a transition in a state associated with an inventory and an associated reward in the form of revenues generated from perishable resource sales. A randomized batch of observations is periodically sampled according to a prioritized replay sampling algorithm. A probability distribution for selection of observations within the batch is progressively adapted. Each batch of observations is used to update weight parameters of a neural network that comprises an approximator of the resource management agent, such that when provided with an input inventory state and an input action, an output of the neural network more closely approximates a true value of generating the input action while in the input inventory state. The neural network may be used to select each generated action depending upon a corresponding state associated with the inventory.
First Claim
Family
International Classification(s)
- [Classification Symbol]
- [Patents Count]
Cited Art Landscape
Patent Citation Ranking
Recipient Email Address
Recipient Email Address
Comment
Recipient Email Address
Add to Portfolio(s)
To add this patent to one, or more, of your portfolios, simply click the add button.
This Patent is in these Portfolios:
Add to additional portfolios:
Last Refreshed On:
Changes done successfully