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Found 8 papers, 3 papers with code
BenchMARL: Benchmarking Multi-Agent Reinforcement Learning
The field of Multi-Agent Reinforcement Learning (MARL) is currently facing a reproducibility crisis.
TorchRL: A data-driven decision-making library for PyTorch
PyTorch has ascended as a premier machine learning framework, yet it lacks a native and comprehensive library for decision and control tasks suitable for large development teams dealing with complex real-world data and environments.
CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning
On a real robot setup, CACTI enables efficient training of a single policy that can perform 10 manipulation tasks involving kitchen objects, and is robust to varying layouts of distractors.
Viscos Flows: Variational Schur Conditional Sampling With Normalizing Flows
We present a method for conditional sampling for pre-trained normalizing flows when only part of an observation is available.
Efficient Semi-Implicit Variational Inference
In this paper, we propose CI-VI an efficient and scalable solver for semi-implicit variational inference (SIVI).
SAMBA: Safe Model-Based & Active Reinforcement Learning
In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics.
$¶$ILCRO: Making Importance Landscapes Flat Again
This paper shows that most of the existing convolutional architectures define, at initialisation, a specific feature importance landscape that conditions their capacity to attend to different locations of the images later during training or even at test time.
The Hierarchical Adaptive Forgetting Variational Filter
A common problem in Machine Learning and statistics consists in detecting whether the current sample in a stream of data belongs to the same distribution as previous ones, is an isolated outlier or inaugurates a new distribution of data.
