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Simultaneously mapping and exploring a complex unknown scene is an NP-hard problem, which is still challenging with the rapid development of deep learning techniques. We present CSO, a deep reinforcement learning-based framework for efficient active ...

Integrated ranking is a critical component in industrial recommendation platforms. It combines candidate lists from different upstream channels or sources and ranks them into an integrated list, which will be exposed to users. During this process, to ...

This research introduces a novel setting for reinforcement learning with constraints, termed Relaxed Exploration Constrained Reinforcement Learning (RECRL). Similar to standard constrained reinforcement learning (CRL), the objective in RECRL is to ...

This extended abstract introduces a novel setting of reinforcement learning with constraints, called Relaxed Exploration Constrained Reinforcement Learning (RECRL). As in standard constrained reinforcement learning (CRL), the aim is to find a policy that ...

The wide popularity of short videos on social media poses new opportunities and challenges to optimize recommender systems on the video-sharing platforms. Users sequentially interact with the system and provide complex and multi-faceted responses, ...

This paper proposes SCALES, a general framework that translates well-established fairness principles into a common representation based on the Constraint Markov Decision Process (CMDP). With the help of causal language, our framework can place ...

Multi-agent reinforcement learning has gained lot of popularity primarily owing to the success of deep function approximation architectures. However, many real-life multi-agent applications often impose constraints on the joint action sequence that can ...

Constrained action-based decision-making is one of the most challenging decision-making problems. It refers to a scenario where an agent takes action in an environment not only to maximize the expected cumulative reward but where it is subject to ...