Reinforcement learning is a learning paradigm for solving sequential decision-making
problems. Recent years have witnessed remarkable progress in reinforcement learning …

群体智能(Collectire intelligence, CI) 系统具有广泛的应用前景. 当前的群体智能决策方法主要
包括知识驱动, 数据驱动两大类, 但各自存在优缺点. 本文指出, 知识与数据协同驱动将为群体 …

Reward shaping is an effective technique for incorporating domain knowledge into
reinforcement learning (RL). Existing approaches such as potential-based reward shaping …

In this paper, we propose a deep reinforcement learning (DRL)-based method that allows
unmanned aerial vehicles (UAVs) to execute navigation tasks in large-scale complex …

We propose RUDDER, a novel reinforcement learning approach for delayed rewards in
finite Markov decision processes (MDPs). In MDPs the Q-values are equal to the expected …

In a connected traffic environment with signalized intersections, eco-driving control needs to
co-optimize fuel economy (fuel consumption), driving safety (collisions and red lights), and …

Human-centered reinforcement learning (RL), in which an agent learns how to perform a
task from evaluative feedback delivered by a human observer, has become more and more …

In lifelong reinforcement learning, agents must effectively transfer knowledge across tasks
while simultaneously addressing exploration, credit assignment, and generalization. State …

Deep reinforcement learning (DRL) has proven to be an effective, general-purpose
technology to develop 'good'replenishment policies in inventory management. We show …

The objective of a reinforcement learning agent is to behave so as to maximise the sum of a
suitable scalar function of state: the reward. These rewards are typically given and …