1 citations · 1 across the 7 of their papers we have counts for
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Generalization in offline RL: The structure is more important than the amount of pessimism
Max Weltevrede, Matthijs T. J. Spaan, Wendelin Böhmer
While pessimism counteracts overestimation bias in offline reinforcement learning (RL), being overly conservative has been associated with hindering certain forms of generalization…
Sparse Masked Attention Policies for Reliable Generalization
Caroline Horsch, Laurens Engwegen, Max Weltevrede +2
In reinforcement learning, abstraction methods that remove unnecessary information from the observation are commonly used to learn policies which generalize better to unseen tasks.…
Universal Value-Function Uncertainties
Moritz A. Zanger, Max Weltevrede, Yaniv Oren +4
Estimating epistemic uncertainty in value functions is a crucial challenge for many aspects of reinforcement learning (RL), including efficient exploration, safe decision-making, a…
How Ensembles of Distilled Policies Improve Generalisation in Reinforcement Learning
Max Weltevrede, Moritz A. Zanger, Matthijs T. J. Spaan +1
In the zero-shot policy transfer setting in reinforcement learning, the goal is to train an agent on a fixed set of training environments so that it can generalise to similar, but…
Training on Irrelevant States Implies Data Augmentation: Generalization in Contextual MDPs
Max Weltevrede, Caroline Horsch, Matthijs T. J. Spaan +1
In the zero-shot policy transfer (ZSPT) setting for contextual Markov decision processes (CMDP), agents train on a fixed, finite set of contexts and must generalize to new ones. Re…
Explore-Go: Leveraging Exploration for Generalisation in Deep Reinforcement Learning
Max Weltevrede, Felix Kaubek, Matthijs T. J. Spaan +1
One of the remaining challenges in reinforcement learning is to develop agents that can generalise to novel scenarios they might encounter once deployed. This challenge is often fr…
The Role of Diverse Replay for Generalisation in Reinforcement Learning
Max Weltevrede, Matthijs T. J. Spaan, Wendelin Böhmer
In reinforcement learning (RL), key components of many algorithms are the exploration strategy and replay buffer. These strategies regulate what environment data is collected and t…