arXiv research

A framework for learning disentangled representations of symmetric environments.

Equivariant CNNs improve RL performance in symmetric environments.

Solvents can induce helical knots in simulated biopolymer tubes.

Framework uncovers symmetric and asymmetric species associations from data.

Transfer learning benefits vary in DRL, affecting representation and policy learning.

Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network learning to more human realistic settings. In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off …

Recent reinforcement learning algorithms, though achieving impressive results in various fields, suffer from brittle training effects such as regression in results and high sensitivity to initialization and parameters. We claim that some of the brittleness stems from variance differences, i.e. when different environmen…

Novel unsupervised MIG detectors improve signal detection in cluttered environments.

In this paper we propose a highly efficient and very accurate deep learning method for estimating the propagation pathloss from a point $x$ (transmitter location) to any point $y$ on a planar domain. For applications such as user-cell site association and device-to-device link scheduling, an accurate knowledge of the p…

Deep Q-Learning optimizes market making by balancing price risk and spread profits.

Two algorithms improve Federated RL in diverse environments.

OBSER framework infers sub-environments from objects, outperforming scene-based methods.

UAED discovers adaptive environments for robust learning.

New approach handles stochastic and partially-observable environments using discrete autoencoders and Monte Carlo tree search.

Self-supervised policy adapts after deployment without rewards.

Bayesian model for multi-environment prediction with latent variable changes.

SVDD and Deep SVDD improve radar target detection in clutter.

This paper introduces CENIE to quantify environment novelty for better UED.

We consider apprenticeship learning, i.e., having an agent learn a task by observing an expert demonstrating the task in a partially observable environment when the model of the environment is uncertain. This setting is useful in applications where the explicit modeling of the environment is difficult, such as a dialog…

Infinite hierarchical contrastive clustering identifies personal environments linked to health outcomes.

Research shows collective learning across diverse environments is hard due to privacy and security concerns.

LEADS improves model generalization across different environments.

A new method shapes reinforcement learning environments by abstracting large state spaces.

In reinforcement learning algorithms, it is a common practice to account for only a single view of the environment to make the desired decisions; however, utilizing multiple views of the environment can help to promote the learning of complicated policies. Since the views may frequently suffer from partial observabilit…

MiniHack simplifies creation of complex RL environments.

WILD-SCAV benchmarks AI in complex 3D FPS environments.

Reinforcement learning aims at searching the best policy model for decision making, and has been shown powerful for sequential recommendations. The training of the policy by reinforcement learning, however, is placed in an environment. In many real-world applications, however, the policy training in the real environmen…

PSRL extension for continuing environments reduces regret.

The paper classifies symmetric triads with multiplicities and their applications.

We find all Ricci semi-symmetric as well as all conformally semi-symmetric spacetimes. Neither of these properties implies the other. We verify that only conformally flat spacetimes can be Ricci semi-symmetric without being conformally semi-symmetric and show that only vacuum spacetimes and spacetimes with just a $Λ$-t…

New method learns robust representations by modeling environment variation.

GALA framework learns invariant graph representations via environment augmentation with minimal assumptions.

We establish a new symmetrization procedure for the isoperimetric problem in symmetric spaces of noncompact type. This symmetrization generalizes the well known Steiner symmetrization in euclidean space. In contrast to the classical construction the symmetrized domain is obtained by solving a nonlinear elliptic equatio…

Reinforcement learning (RL) methods learn optimal decisions in the presence of a stationary environment. However, the stationary assumption on the environment is very restrictive. In many real world problems like traffic signal control, robotic applications, one often encounters situations with non-stationary environme…

Study on totally symmetric sets with group applications.

In this article, we summarize the results on symmetric conformal geometries. We review the results following from the general theory of symmetric parabolic geometries and prove several new results for symmetric conformal geometries. In particular, we show that each symmetric conformal geometry is either locally flat or…

We discuss a variant of Thompson sampling for nonparametric reinforcement learning in a countable classes of general stochastic environments. These environments can be non-Markov, non-ergodic, and partially observable. We show that Thompson sampling learns the environment class in the sense that (1) asymptotically its …

XRM discovers environments without human annotations for OOD methods.

ATLAS separates invariant and transferable latent factors across diverse environments.

In reinforcement learning algorithms, leveraging multiple views of the environment can improve the learning of complicated policies. In multi-view environments, due to the fact that the views may frequently suffer from partial observability, their level of importance are often different. In this paper, we propose a dee…

The object of the present paper is to study locally $φ$-symmetric LP-Sasakian manifolds admitting semi-symmetric metric connection and obtain a necessary and sufficient condition for a locally $φ$-symmetric LP-Sasakian manifold with respect to semi-symmetric metric connection to be locally $φ$-symmetric LP-Sasakian man…

Under appropriate cooperation protocols and parameter choices, fully decentralized solutions for stochastic optimization have been shown to match the performance of centralized solutions and result in linear speedup (in the number of agents) relative to non-cooperative approaches in the strongly-convex setting. More re…

Learning in a non-stationary environment is an inevitable problem when applying machine learning algorithm to real world environment. Learning new tasks without forgetting the previous knowledge is a challenge issue in machine learning. We propose a Kalman Filter based modifier to maintain the performance of Neural Net…

Researchers use DT to transfer policies from one environment to another using causal reasoning.

In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations during training or via use as part of an explicit planning mechanism. However, their application in practice has been limited to simplistic envi…

In this note, we discuss symmetric brackets on skew-symmetric algebroids associated with a metric structure. Given a pseudo-Riemannian metric structure, we describe symmetric brackets induced by connections with totally skew-symmetric torsion in the language of Lie derivatives and differentials of functions. In particu…

ANTIDOTE reduces noisy labels influence during learning.

Symmetric Poisson structures linked to geodesic foliations and Jordan algebras.