PATENT CLAIM ANALYSIS

Application Number: 16417522
Application Type: Utility
Filing Date: 2019-05
Publication Date: 2019-11
Patent Classification: ["706", "025000"]

Abstract:
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for reinforcement learning using agent curricula. One of the methods includes maintaining data specifying plurality of candidate agent policy neural networks; initializing mixing data that assigns a respective weight to each of the candidate agent policy neural networks; training the candidate agent policy neural networks using a reinforcement learning technique to generate combined action selection policies that result in improved performance on a reinforcement learning task; and during the training, repeatedly adjusting the weights in the mixing data to favor higher-performing candidate agent policy neural networks.

Claim (Index 17):
The system of  claim 12 , wherein generating, using the candidate agent policy neural networks and in accordance with the weights in the mixing data as of the training iteration, a combined action selection policy using the training network input comprises:\n processing the training network input using each of the candidate agent policy neural networks to generate a respective action selection policy for each candidate agent policy neural network; and combining the action selection policies in accordance with the weights as of the training iteration to generate the combined action selection policy.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 100.0
- Lexical Diversity: 1.8
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['16380125', '15499832', '14097862', '15704969', '15977923']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3741028753758736
- 35 USC 102 Novelty (BERT): 0.5127750513117937
- Combined Prediction Score: 0.3879700929694657
- Mean Citation Score: 249.24804
- Max Citation Score: 270.2168
- Similarity Product: 185.64676288809773

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

Dataset: test