Concerned with multi-objective reinforcement learning (MORL), this paper presents MO-MCTS, an extension of Monte-Carlo Tree Search to multi-objective sequential decision making. The known multi-objective indicator referred to as hyper-volume indicator is used to define an action selection criterion

to multi-object checking – the automated generation of a global system trace, if a specification in multi-object logic does not hold.

Abstract This paper presents a bag of feature based method for visual object recognition. Our contribution is focussed on the selection of the best feature descriptor. It is implemented by using a novel multi-table reinforcement learning method that selects among five of classical descriptors (i

Concerned with multi-objective reinforcement learning (MORL), this paper presents MO-MCTS, an extension of Monte-Carlo Tree Search to multi-objective sequential decision making. The known multi-objective indicator referred to as hyper-volume indicator is used to define an action selection criterion

the multi-objective multi-agent reinforcement learning setup. The analysed approaches intel-ligently explore the weight-space in order to find a wider range of system trade-offs. In our second contribution, we propose a novel adaptive weight algorithm which interacts with the underlying local multi-objective

objective reinforcement learning (MORL) is proposed in this paper. The IEEE 14 Bus was used to validate the effectiveness of the proposed CRRED formulation and Hybrid method.The numerical results obtained show that combining real and reactive power results in a 0.95 % decrease in the overall generation cost

We present a new approach for activity modelling and anomaly detection based on non-parametric Gaussian Process (GP) models. Specifically, GP regression models are formulated to learn non-linear relationships between multi-object activity patterns observed from semantically decomposed regions

of the objects. In this paper, we propose a novel clustering approach for clustering multi-type interrelated data objects, ReCoM (Reinforcement Clustering of Multi-type Interrelated data objects). Under this approach, relationships among data objects are used to improve the cluster quality of interrelated data

to the dynamic nature of streaming environments, the performance objective may need to change dynamically. Furthermore, the performance specification defined by users may itself be multi-dimensional. Therefore, utilizing a single scheduling algorithm optimized for a single objective is no longer sufficient

are then provided to a new multi-objective long short term memory network (LSTM) to infer the intent class and slot tags. Our empirical investigations on CMN show impressive gains over the end-to-end LSTM baselines on ATIS dataset as well as two other humanto-machine conversational datasets. Index Terms