12 citations · 13 across the 6 of their papers we have counts for
6 papers
Partial Identifiability in Inverse Reinforcement Learning For Agents With Non-Exponential Discounting
Joar Skalse, Alessandro Abate
The aim of inverse reinforcement learning (IRL) is to infer an agent's preferences from observing their behaviour. Usually, preferences are modelled as a reward function, , and…
Partial Identifiability and Misspecification in Inverse Reinforcement Learning
Joar Skalse, Alessandro Abate
The aim of Inverse Reinforcement Learning (IRL) is to infer a reward function from a policy . This problem is difficult, for several reasons. First of all, there are typical…
The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret
Lukas Fluri, Leon Lang, Alessandro Abate +3
In reinforcement learning, specifying reward functions that capture the intended task can be very challenging. Reward learning aims to address this issue by learning the reward fun…
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems
David "davidad" Dalrymple, Joar Skalse, Yoshua Bengio +14
Ensuring that AI systems reliably and robustly avoid harmful or dangerous behaviours is a crucial challenge, especially for AI systems with a high degree of autonomy and general in…
Quantifying the Sensitivity of Inverse Reinforcement Learning to Misspecification
Joar Skalse, Alessandro Abate
Inverse reinforcement learning (IRL) aims to infer an agent's preferences (represented as a reward function ) from their behaviour (represented as a policy ). To do this, we…
STARC: A General Framework For Quantifying Differences Between Reward Functions
Joar Skalse, Lucy Farnik, Sumeet Ramesh Motwani +3
In order to solve a task using reinforcement learning, it is necessary to first formalise the goal of that task as a reward function. However, for many real-world tasks, it is very…