PATENT CLAIM ANALYSIS

Application Number: 15977923
Application Type: Utility
Filing Date: 2018-05
Publication Date: 2018-09
Patent Classification: ["706", "025000"]

Abstract:
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for asynchronous deep reinforcement learning. One of the systems includes a plurality of workers, wherein each worker is configured to operate independently of each other worker, and wherein each worker is associated with a respective actor that interacts with a respective replica of the environment during the training of the deep neural network.

Claim (Index 8):
The system of  claim 6 , wherein the reinforcement learning technique is a SARSA technique, and wherein performing the iteration comprises:\n selecting a next action to be performed by the actor associated with the worker in response to the next observation in accordance with the respective action selection policy for the worker; processing the next action and the next observation using a target network to determine a target network output for the next action, wherein the target network is the same neural network as the Q network but with possibly different parameter values; determining an error from the target network output for the next action, the actual reward, and the Q value for the current action; and determining the current gradient using the error.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 93.0
- Lexical Diversity: 1.58696
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15016173', '15367094', '15349900', '15977913', '15349894']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4012034489528501
- 35 USC 102 Novelty (BERT): 0.5135512551143919
- Combined Prediction Score: 0.4124382295690043
- Mean Citation Score: 225.962432
- Max Citation Score: 296.19855
- Similarity Product: 201.22492678624388

Labels:
- Claim Label 101: 0
- Claim Label 102: 1
- Claim Label 103: 0
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 0

Dataset: test