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

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 …

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

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 …

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

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

Reinforcement Learning (RL) seeks to develop systems capable of autonomous decision-
making by learning through interaction with their environment. Central to this process are …

We study the design of explicable reward functions for a reinforcement learning agent while
guaranteeing that an optimal policy induced by the function belongs to a set of target …