7 citations · 21 across the 18 of their papers we have counts for
12 papers · 1 filter
Causal Discovery in Action: Learning Chain-Reaction Mechanisms from Interventions
Panayiotis Panayiotou, Özgür Şimşek
Causal discovery is challenging in general dynamical systems because, without strong structural assumptions, the underlying causal graph may not be identifiable even from intervent…
CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning
Panayiotis Panayiotou, Audrey Poinsot, Alessandro Leite +3
Causal machine learning (Causal ML) aims to answer "what if" questions using machine learning algorithms, making it a promising tool for high-stakes decision-making. Yet, empirical…
Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
Audrey Poinsot, Panayiotis Panayiotou, Alessandro Leite +3
Causal machine learning has the potential to revolutionize decision-making by combining the predictive power of machine learning algorithms with the theory of causal inference. How…
Learning The Minimum Action Distance
Lorenzo Steccanella, Joshua B. Evans, Özgür Şimşek +1
This paper presents a state representation framework for Markov decision processes (MDPs) that can be learned solely from state trajectories, requiring neither reward signals nor t…
A Theoretical Framework for Explaining Reinforcement Learning with Shapley Values
Daniel Beechey, Thomas M. S. Smith, Özgür Şimşek
Reinforcement learning agents can achieve super-human performance in complex decision-making tasks, but their behaviour is often difficult to understand and explain. This lack of e…
Hierarchical Orchestra of Policies
Thomas P Cannon, Özgür Simsek
Continual reinforcement learning poses a major challenge due to the tendency of agents to experience catastrophic forgetting when learning sequential tasks. In this paper, we intro…
Curricula for Learning Robust Policies with Factored State Representations in Changing Environments
Panayiotis Panayiotou, Özgür Şimşek
Robust policies enable reinforcement learning agents to effectively adapt to and operate in unpredictable, dynamic, and ever-changing real-world environments. Factored representati…
Colour versus Shape Goal Misgeneralization in Reinforcement Learning: A Case Study
Karolis Ramanauskas, Özgür Şimşek
We explore colour versus shape goal misgeneralization originally demonstrated by Di Langosco et al. (2022) in the Procgen Maze environment, where, given an ambiguous choice, the ag…
Explaining Reinforcement Learning with Shapley Values
Daniel Beechey, Thomas M. S. Smith, Özgür Şimşek
For reinforcement learning systems to be widely adopted, their users must understand and trust them. We present a theoretical analysis of explaining reinforcement learning using Sh…
Creating Multi-Level Skill Hierarchies in Reinforcement Learning
Joshua B. Evans, Özgür Şimşek
What is a useful skill hierarchy for an autonomous agent? We propose an answer based on a graphical representation of how the interaction between an agent and its environment may u…
Iterative Policy-Space Expansion in Reinforcement Learning
Jan Malte Lichtenberg, Özgür Şimşek
Humans and animals solve a difficult problem much more easily when they are presented with a sequence of problems that starts simple and slowly increases in difficulty. We explore…
The Game of Tetris in Machine Learning
Simón Algorta, Özgür Şimşek
The game of Tetris is an important benchmark for research in artificial intelligence and machine learning. This paper provides a historical account of the algorithmic developments…