5 papers · 1 filter
Boosting deep Reinforcement Learning using pretraining with Logical Options
Zihan Ye, Phil Chau, Raban Emunds +5
Deep reinforcement learning agents are often misaligned, as they over-exploit early reward signals. Recently, several symbolic approaches have addressed these challenges by encodin…
Better Decisions through the Right Causal World Model
Elisabeth Dillies, Quentin Delfosse, Jannis Blüml +3
Reinforcement learning (RL) agents have shown remarkable performances in various environments, where they can discover effective policies directly from sensory inputs. However, the…
Problem Solving Through Human-AI Preference-Based Cooperation
Subhabrata Dutta, Timo Kaufmann, Goran Glavaš +7
While there is a widespread belief that artificial general intelligence (AGI) -- or even superhuman AI -- is imminent, complex problems in expert domains are far from being solved.…
HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning
Quentin Delfosse, Jannis Blüml, Bjarne Gregori +1
Artificial agents' adaptability to novelty and alignment with intended behavior is crucial for their effective deployment. Reinforcement learning (RL) leverages novelty as a means…
Amplifying Exploration in Monte-Carlo Tree Search by Focusing on the Unknown
Cedric Derstroff, Jannis Brugger, Jannis Blüml +3
Monte-Carlo tree search (MCTS) is an effective anytime algorithm with a vast amount of applications. It strategically allocates computational resources to focus on promising segmen…