5 citations · 7 across the 4 of their papers we have counts for
7 papers
AI2STOW: End-to-End Deep Reinforcement Learning to Construct Master Stowage Plans under Demand Uncertainty
Jaike Van Twiller, Djordje Grbic, Rune Møller Jensen
The worldwide economy and environmental sustainability depend on eff icient and reliable supply chains, in which container shipping plays a crucial role as an environmentally frien…
Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage Planning
Jaike van Twiller, Yossiri Adulyasak, Erick Delage +2
Reinforcement learning (RL) has successfully solved various deterministic and stochastic planning problems. However, conventional RL struggles with complex real-world constraints,…
Procedurally generating rules to adapt difficulty for narrative puzzle games
Thomas Volden, Djordje Grbic, Paolo Burelli
This paper focuses on procedurally generating rules and communicating them to players to adjust the difficulty. This is part of a larger project to collect and adapt games in educa…
Safer Reinforcement Learning through Transferable Instinct Networks
Djordje Grbic, Sebastian Risi
Random exploration is one of the main mechanisms through which reinforcement learning (RL) finds well-performing policies. However, it can lead to undesirable or catastrophic outco…
Growing 3D Artefacts and Functional Machines with Neural Cellular Automata
Shyam Sudhakaran, Djordje Grbic, Siyan Li +4
Neural Cellular Automata (NCAs) have been proven effective in simulating morphogenetic processes, the continuous construction of complex structures from very few starting cells. Re…
EvoCraft: A New Challenge for Open-Endedness
Djordje Grbic, Rasmus Berg Palm, Elias Najarro +2
This paper introduces EvoCraft, a framework for Minecraft designed to study open-ended algorithms. We introduce an API that provides an open-source Python interface for communicati…
Safe Reinforcement Learning through Meta-learned Instincts
Djordje Grbic, Sebastian Risi
An important goal in reinforcement learning is to create agents that can quickly adapt to new goals while avoiding situations that might cause damage to themselves or their environ…