91 citations · 132 across the 9 of their papers we have counts for
8 papers · 1 filter
Super Agents and Confounders: Influence of surrounding agents on vehicle trajectory prediction
Daniel Jost, Luca Paparusso, Martin Stoll +3
In highly interactive driving scenes, trajectory prediction is conditioned on information from surrounding traffic participants such as cars and pedestrians. Our main contribution…
One-shot World Models Using a Transformer Trained on a Synthetic Prior
Fabio Ferreira, Moreno Schlageter, Raghu Rajan +2
A World Model is a compressed spatial and temporal representation of a real world environment that allows one to train an agent or execute planning methods. However, world models a…
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning
Jannis Becktepe, Julian Dierkes, Carolin Benjamins +7
Hyperparameters are a critical factor in reliably training well-performing reinforcement learning (RL) agents. Unfortunately, developing and evaluating automated approaches for tun…
Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalization
Sai Prasanna, Karim Farid, Raghu Rajan +1
Zero-shot generalization (ZSG) to unseen dynamics is a major challenge for creating generally capable embodied agents. To address the broader challenge, we start with the simpler s…
Automated Reinforcement Learning (AutoRL): A Survey and Open Problems
Jack Parker-Holder, Raghu Rajan, Xingyou Song +9
The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable a…
TempoRL: Learning When to Act
André Biedenkapp, Raghu Rajan, Frank Hutter +1
Reinforcement learning is a powerful approach to learn behaviour through interactions with an environment. However, behaviours are usually learned in a purely reactive fashion, whe…
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
Baohe Zhang, Raghu Rajan, Luis Pineda +5
Model-based Reinforcement Learning (MBRL) is a promising framework for learning control in a data-efficient manner. MBRL algorithms can be fairly complex due to the separate dynami…
MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning
Raghu Rajan, Jessica Lizeth Borja Diaz, Suresh Guttikonda +4
We present MDP Playground, a testbed for Reinforcement Learning (RL) agents with dimensions of hardness that can be controlled independently to challenge agents in different ways a…