In this paper, we propose a novel approach of model-based learning on state-based
potential games (MB-SbPGs) that enables distributed self-optimization of manufacturing …

Reinforcement learning (RL) is increasingly adopted in job shop scheduling problems
(JSSP). But RL for JSSP is usually done using a vectorized representation of machine …

Abstract Autonomous Surface Vehicles (ASVs) are bound to play a fundamental role in the
maintenance of offshore wind farms. Robust navigation for inspection vehicles should take …

In this research, we discuss the impact of combining model-based deep learning and game
theory in dynamic games to develop a sample-efficient self-learning methodology for smart …

Unmanned Aerial Vehicles (UAVs) have several military and civilian applications to perform
tasks that do not require a central processing unit or human involvement. There are various …

Deep reinforcement learning (DRL) benefits from the representation power of deep neural
networks (NNs), to approximate the value function and policy in the learning process. Batch …

Value function approximation, such as Q-learning, is widely used in the discrete control
rather than the continuous one because the optimal action in the discrete setting is more …

This paper introduces a novel combination of scheduling control on a flexible robot
manufacturing cell with curiosity based RL. Reinforcement learning has proved to be highly …

This paper presents a novel approach to train a Reinforcement Learning (RL) agent faster
for transportation of parcels in a Peristaltic Sortation Machine (PSM) using curriculum …

This paper introduces a novel combination of scheduling control on a flexible robot
manufacturing cell with curiosity based reinforcement learning. Reinforcement learning has …