Scaling Ant Colony Optimization with Hierarchical Reinforcement Learning Partitioning

This research merges the hierarchical reinforcement learning (HRL) domain and the ant colony optimization (ACO) domain. The merger produces a HRL ACO algorithm capable of generating solutions for both domains. This research also provides two specific implementations of the new algorithm: the first a modification to Dietterich\u27s MAXQ-Q HRL algorithm, the second a hierarchical ACO algorithm. These implementations generate faster results, with little to no significant change in the quality of solutions for the tested problem domains. The application of ACO to the MAXQ-Q algorithm replaces the reinforcement learning, Q-learning and SARSA, with the modified ant colony optimization method, Ant-Q. This algorithm, MAXQ-AntQ, converges to solutions not significantly different from MAXQ-Q in 88% of the time. This research then transfers HRL techniques to the ACO domain and traveling salesman problem (TSP). To apply HRL to ACO, a hierarchy must be created for the TSP. A data clustering algorithm creates these subtasks, with an ACO algorithm to solve the individual and complete problems. This research tests two clustering algorithms, k-means and G-means. The results demonstrate the algorithm with data clustering produces solutions 85-95% faster but with 5-10% decrease in solution quality

Scaling Ant Colony Optimization with Hierarchical Reinforcement Learning Partitioning

This paper merges hierarchical reinforcement learning (HRL) with ant colony optimization (ACO) to produce a HRL ACO algorithm capable of generating solutions for large domains. This paper describes two specific implementations of the new algorithm: the first a modification to Dietterich’s MAXQ-Q HRL algorithm, the second a hierarchical ant colony system algorithm. These implementations generate faster results, with little to no significant change in the quality of solutions for the tested problem domains. The application of ACO to the MAXQ-Q algorithm replaces the reinforcement learning, Q-learning, with the modified ant colony optimization method, Ant-Q. This algorithm, MAXQ-AntQ, converges to solutions not significantly different from MAXQ-Q in 88% of the time. This paper then transfers HRL techniques to the ACO domain and traveling salesman problem (TSP). To apply HRL to ACO, a hierarchy must be created for the TSP. A data clustering algorithm creates these subtasks, with an ACO algorithm to solve the individual and complete problems. This paper tests two clustering algorithms, k-means and G-means. The results demonstrate the algorithm with data clustering produces solutions 20 times faster with 5-10% decrease in solution quality due to the effects of clustering

Reconciling biodiversity and carbon stock conservation in an Afrotropical forest landscape

Protecting aboveground carbon stocks in tropical forests is essential for mitigating global climate change and is assumed to simultaneously conserve biodiversity. Although the relationship between tree diversity and carbon stocks is generally positive, the relationship remains unclear for consumers or decomposers. We assessed this relationship for multiple trophic levels across the tree of life (10 organismal groups, 3 kingdoms) in lowland rainforests of the Congo Basin. Comparisons across regrowth and old-growth forests evinced the expected positive relationship for trees, but not for other organismal groups. Moreover, differences in species composition between forests increased with difference in carbon stock. These variable associations across the tree of life contradict the implicit assumption that maximum co-benefits to biodiversity are associated with conservation of forests with the highest carbon storage. Initiatives targeting climate change mitigation and biodiversity conservation should include both old-growth and regenerating forests to optimally benefit biodiversity and carbon storage

Competence in Endoscopic Ultrasound and Endoscopic Retrograde Cholangiopancreatography, From Training Through Independent Practice.

BACKGROUND & AIMS: It is unclear whether participation in competency-based fellowship programs for endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP) results in high-quality care in independent practice. We measured quality indicator (QI) adherence during the first year of independent practice among physicians who completed endoscopic training with a systematic assessment of competence. METHODS: We performed a prospective multicenter cohort study of invited participants from 62 training programs. In phase 1, 24 advanced endoscopy trainees (AETs), from 20 programs, were assessed using a validated competence assessment tool. We used a comprehensive data collection and reporting system to create learning curves using cumulative sum analysis that were shared with AETs and trainers quarterly. In phase 2, participating AETs entered data into a database pertaining to every EUS and ERCP examination during their first year of independent practice, anchored by key QIs. RESULTS: By the end of training, most AETs had achieved overall technical competence (EUS 91.7%, ERCP 73.9%) and cognitive competence (EUS 91.7%, ERCP 94.1%). In phase 2 of the study, 22 AETs (91.6%) participated and completed a median of 136 EUS examinations per AET and 116 ERCP examinations per AET. Most AETs met the performance thresholds for QIs in EUS (including 94.4% diagnostic rate of adequate samples and 83.8% diagnostic yield of malignancy in pancreatic masses) and ERCP (94.9% overall cannulation rate). CONCLUSIONS: In this prospective multicenter study, we found that although competence cannot be confirmed for all AETs at the end of training, most meet QI thresholds for EUS and ERCP at the end of their first year of independent practice. This finding affirms the effectiveness of training programs. Clinicaltrials.gov ID NCT02509416

Competence in Endoscopic Ultrasound and Endoscopic Retrograde Cholangiopancreatography, From Training Through Independent Practice.

BACKGROUND & AIMS: It is unclear whether participation in competency-based fellowship programs for endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP) results in high-quality care in independent practice. We measured quality indicator (QI) adherence during the first year of independent practice among physicians who completed endoscopic training with a systematic assessment of competence. METHODS: We performed a prospective multicenter cohort study of invited participants from 62 training programs. In phase 1, 24 advanced endoscopy trainees (AETs), from 20 programs, were assessed using a validated competence assessment tool. We used a comprehensive data collection and reporting system to create learning curves using cumulative sum analysis that were shared with AETs and trainers quarterly. In phase 2, participating AETs entered data into a database pertaining to every EUS and ERCP examination during their first year of independent practice, anchored by key QIs. RESULTS: By the end of training, most AETs had achieved overall technical competence (EUS 91.7%, ERCP 73.9%) and cognitive competence (EUS 91.7%, ERCP 94.1%). In phase 2 of the study, 22 AETs (91.6%) participated and completed a median of 136 EUS examinations per AET and 116 ERCP examinations per AET. Most AETs met the performance thresholds for QIs in EUS (including 94.4% diagnostic rate of adequate samples and 83.8% diagnostic yield of malignancy in pancreatic masses) and ERCP (94.9% overall cannulation rate). CONCLUSIONS: In this prospective multicenter study, we found that although competence cannot be confirmed for all AETs at the end of training, most meet QI thresholds for EUS and ERCP at the end of their first year of independent practice. This finding affirms the effectiveness of training programs. Clinicaltrials.gov ID NCT02509416