Jiali Lv
Biographic Data
| ID | 8364304 |
|---|---|
| NAME | Jiali Lv |
| GIVEN NAMES | Jiali |
| FAMILY NAME | Lv |
| SIGNATURE | LV J |
| AFFILIATIONS | Shandong University |
| ORCID | 0009-0003-2706-5613 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Tpgca
Multiagent reinforcement learning (MARL) methods have good application performances and prospects in cooperative tasks. To improve the capability of agent policy learning in new scenarios, some methods transfer the learned policy knowledge to new scenarios. However, most methods only focus on the knowledge transfer of individual agent policies, neglecting the credit assignment among agents in cooperative tasks, which results in a transfer bias of…
Association between household solid fuel usage and trajectories of multimorbidity among middle-aged and older adults
Background This study aimed to explore the effect of household solid fuel usage on the multimorbidity trajectories among middle-aged and older adults. Methods Based on the 2011–2018 China Health and Retirement Longitudinal Study, the group-based trajectory modeling and the multinomial logistic regression model were used to explore the relationship between multimorbidity trajectories of older adults with different fuel types, duration of solid fue…
No prominent works on this page.
Association between household solid fuel usage and trajectories of multimorbidity among middle-aged and older adults
Background This study aimed to explore the effect of household solid fuel usage on the multimorbidity trajectories among middle-aged and older adults. Methods Based on the 2011–2018 China Health and Retirement Longitudinal Study, the group-based trajectory modeling and the multinomial logistic regression model were used to explore the relationship between multimorbidity trajectories of older adults with different fuel types, duration of solid fue…
Tpgca
Multiagent reinforcement learning (MARL) methods have good application performances and prospects in cooperative tasks. To improve the capability of agent policy learning in new scenarios, some methods transfer the learned policy knowledge to new scenarios. However, most methods only focus on the knowledge transfer of individual agent policies, neglecting the credit assignment among agents in cooperative tasks, which results in a transfer bias of…
Air Quality and Health Impacts (1 work) · Cohort (1 work) · Cohort study (1 work) · Confidence interval (1 work) · Decomposition (1 work) · Demography (1 work) · Domain Adaptation and Few-Shot Learning (1 work) · Energy and Environment Impacts (1 work) · Environmental health (1 work) · Explainable Artificial Intelligence (XAI (1 work)