.png){kind=icon}
返回
Dynamic programming with meta-reinforcement learning: a novel approach for multi-objective optimization
.png){kind=icon}
OA
AI
DOI：10.1007/s40747-024-01469-1.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multi-objective optimization (MOO) endeavors to identify optimal solutions from a finite array of possibilities. In recent years, deep reinforcement learning (RL) has exhibited promise through its well-crafted heuristics in tackling NP-hard combinatorial optimization (CO) problems. Nonetheless, current methodologies grapple with two key challenges: (1) They primarily concentrate on single-objective optimization quandaries, rendering them less adaptable to the more prevalent MOO scenarios encountered in real-world applications. (2) These approaches furnish an approximate solution by imbibing heuristics, lacking a systematic means to enhance or substantiate optimality. Given these challenges, this study introduces an overarching hybrid strategy, dynamic programming with meta-reinforcement learning (DPML), to resolve MOO predicaments. The approach melds meta-learning into an RL framework, addressing multiple subproblems inherent to MOO. Furthermore, the precision of solutions is elevated by endowing exact dynamic programming with the prowess of meta-graph neural networks. Empirical results substantiate the supremacy of our methodology over previous RL and heuristics approaches, bridging the chasm between theoretical underpinnings and real-world applicability within this domain.
Keyword:
Combinatorial optimization
Meta-learning
Reinforcement learning
Dynamic programming
期刊
IF:
4.6
论文数:
2.1K
被引数:
6.6K
机构
引用论文
Is there a postnormal time? From the illusion of normality to the design for a new normality.png)
Futures
FuturesIF0
PREAI
分享收藏Interpretation of Radio-Echo Returns from Internal Water Bodies in Variegated Glacier, Alaska, U.S.A.美国阿拉斯加杂色冰川内部水体的无线电回波回波解释OAAI
OAAI分享收藏分享收藏分享收藏
查看更多内容