91 citations · 132 across the 9 of their papers we have counts for
9 papers
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…
T3VIP: Transformation-based 3D Video Prediction
Iman Nematollahi, Erick Rosete-Beas, Seyed Mahdi B. Azad +3
For autonomous skill acquisition, robots have to learn about the physical rules governing the 3D world dynamics from their own past experience to predict and reason about plausible…
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…