Dissecting Deep RL with High Update Ratios: Combatting Value Overestimation and Divergence

no code implementations • 9 Mar 2024 • Marcel Hussing, Claas Voelcker, Igor Gilitschenski, Amir-Massoud Farahmand, Eric Eaton

We show that deep reinforcement learning can maintain its ability to learn without resetting network parameters in settings where the number of gradient updates greatly exceeds the number of environment samples.

Robotic Manipulation Datasets for Offline Compositional Reinforcement Learning

1 code implementation • 13 Jul 2023 • Marcel Hussing, Jorge A. Mendez, Anisha Singrodia, Cassandra Kent, Eric Eaton

We provide training and evaluation settings for assessing an agent's ability to learn compositional task policies.

Benchmarking Offline RL +2

Land Use Prediction using Electro-Optical to SAR Few-Shot Transfer Learning

no code implementations • 4 Dec 2022 • Marcel Hussing, Karen Li, Eric Eaton

Satellite image analysis has important implications for land use, urbanization, and ecosystem monitoring.

Contrastive Learning Transfer Learning

CompoSuite: A Compositional Reinforcement Learning Benchmark

1 code implementation • 8 Jul 2022 • Jorge A. Mendez, Marcel Hussing, Meghna Gummadi, Eric Eaton

We present CompoSuite, an open-source simulated robotic manipulation benchmark for compositional multi-task reinforcement learning (RL).

reinforcement-learning Reinforcement Learning (RL)

Structured Object-Aware Physics Prediction for Video Modeling and Planning

1 code implementation • ICLR 2020 • Jannik Kossen, Karl Stelzner, Marcel Hussing, Claas Voelcker, Kristian Kersting

When humans observe a physical system, they can easily locate objects, understand their interactions, and anticipate future behavior, even in settings with complicated and previously unseen interactions.