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Found 9 papers, 1 papers with code
Approximation Algorithms for $\ell_0$-Low Rank Approximation
For small $\psi$, our approximation factor is $1+o(1)$.
Approximation Algorithms for \ell_0-Low Rank Approximation
For small $\psi$, our approximation factor is $1+o(1)$.
A PTAS for $\ell_p$-Low Rank Approximation
On the algorithmic side, for $p \in (0, 2)$, we give the first $(1+\epsilon)$-approximation algorithm running in time $n^{\text{poly}(k/\epsilon)}$.
Physarum-Inspired Multi-Commodity Flow Dynamics
We formulate network design as the problem of constructing a network that efficiently supports a multi-commodity flow problem.
Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motions
Learning diverse skills is one of the main challenges in robotics.
Diverse Offline Imitation Learning
There has been significant recent progress in the area of unsupervised skill discovery, utilizing various information-theoretic objectives as measures of diversity.
Benchmarking Offline Reinforcement Learning on Real-Robot Hardware
To coordinate the efforts of the research community toward tackling this problem, we propose a benchmark including: i) a large collection of data for offline learning from a dexterous manipulation platform on two tasks, obtained with capable RL agents trained in simulation; ii) the option to execute learned policies on a real-world robotic system and a simulation for efficient debugging.
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World
For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and benchmark algorithms.
Learning Diverse Skills for Local Navigation under Multi-constraint Optimality
We demonstrate the effectiveness of our method on a local navigation task where a quadruped robot needs to reach the target within a finite horizon.
