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Found 6 papers, 3 papers with code
Oracle-Efficient Reinforcement Learning for Max Value Ensembles
Reinforcement learning (RL) in large or infinite state spaces is notoriously challenging, both theoretically (where worst-case sample and computational complexities must scale with state space cardinality) and experimentally (where function approximation and policy gradient techniques often scale poorly and suffer from instability and high variance).
Dissecting Deep RL with High Update Ratios: Combatting Value Overestimation and Divergence
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
We provide training and evaluation settings for assessing an agent's ability to learn compositional task policies.
Land Use Prediction using Electro-Optical to SAR Few-Shot Transfer Learning
Satellite image analysis has important implications for land use, urbanization, and ecosystem monitoring.
CompoSuite: A Compositional Reinforcement Learning Benchmark
We present CompoSuite, an open-source simulated robotic manipulation benchmark for compositional multi-task reinforcement learning (RL).
Structured Object-Aware Physics Prediction for Video Modeling and Planning
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.
