scholarly journals Enhancing semantics with multi‐objective reinforcement learning for video description

2021 ◽  
Author(s):  
Qinyu Li ◽  
Longyu Yang ◽  
Pengjie Tang ◽  
Hanli Wang
Keyword(s):  
Reinforcement Learning ◽  
Multi Objective ◽  
Video Description

scholarly journals Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning

2021 ◽  
pp. 1-1
Author(s):  
Stefan Schneider ◽  
Ramin Khalili ◽  
Adnan Manzoor ◽  
Haydar Qarawlus ◽  
Rafael Schellenberg ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Service Coordination ◽  
Multi Objective ◽  
Self Learning

scholarly journals Scalarized Q Multi-Objective Reinforcement Learning for Area Coverage Control and Light Control Implementation

2018 ◽  
Vol 16 (2) ◽  
pp. 72-82
Author(s):  
Akkhachai Phuphanin ◽  
Wipawee Usaha
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Area Coverage ◽  
Coverage Area ◽  
Lighting Control ◽  
Multi Objective ◽  
Coverage Control ◽  
Control Schemes ◽  
Multi Agent ◽  
Testbed Experiments

Coverage control is crucial for the deployment of wireless sensor networks (WSNs). However, most coverage control schemes are based on single objective optimization such as coverage area only, which do not consider other contradicting objectives such as energy consumption, the number of working nodes, wasteful overlapping areas. This paper proposes on a Multi-Objective Optimization (MOO) coverage control called Scalarized Q Multi-Objective Reinforcement Learning (SQMORL). The two objectives are to achieve the maximize area coverage and to minimize the overlapping area to reduce energy consumption. Performance evaluation is conducted for both simulation and multi-agent lighting control testbed experiments. Simulation results show that SQMORL can obtain more efficient area coverage with fewer working nodes than other existing schemes.  The hardware testbed results show that SQMORL algorithm can find the optimal policy with good accuracy from the repeated runs.

Virtual machine placement based on multi-objective reinforcement learning

2020 ◽  
Vol 50 (8) ◽  
pp. 2370-2383 ◽  
Author(s):  
Yao Qin ◽  
Hua Wang ◽  
Shanwen Yi ◽  
Xiaole Li ◽  
Linbo Zhai
Keyword(s):  
Reinforcement Learning ◽  
Virtual Machine ◽  
Virtual Machine Placement ◽  
Multi Objective

Model-Based Multi-Objective Reinforcement Learning by a Reward Occurrence Probability Vector

2020 ◽  
pp. 269-295
Author(s):  
Tomohiro Yamaguchi ◽  
Shota Nagahama ◽  
Yoshihiro Ichikawa ◽  
Yoshimichi Honma ◽  
Keiki Takadama
Keyword(s):  
Reinforcement Learning ◽  
Experimental Results ◽  
Previous Model ◽  
Pareto Optimal ◽  
Occurrence Probability ◽  
Contrast Model ◽  
Multi Objective ◽  
Model Based ◽  
Model Free ◽  
Optimal Policies

This chapter describes solving multi-objective reinforcement learning (MORL) problems where there are multiple conflicting objectives with unknown weights. Previous model-free MORL methods take large number of calculations to collect a Pareto optimal set for each V/Q-value vector. In contrast, model-based MORL can reduce such a calculation cost than model-free MORLs. However, previous model-based MORL method is for only deterministic environments. To solve them, this chapter proposes a novel model-based MORL method by a reward occurrence probability (ROP) vector with unknown weights. The experimental results are reported under the stochastic learning environments with up to 10 states, 3 actions, and 3 reward rules. The experimental results show that the proposed method collects all Pareto optimal policies, and it took about 214 seconds (10 states, 3 actions, 3 rewards) for total learning time. In future research directions, the ways to speed up methods and how to use non-optimal policies are discussed.

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