arXiv research

New framework for PMD convergence in non-tabular environments.

We consider an agent's uncertainty about its environment and the problem of generalizing this uncertainty across observations. Specifically, we focus on the problem of exploration in non-tabular reinforcement learning. Drawing inspiration from the intrinsic motivation literature, we use density models to measure uncert…

The study uses pre-trained neural networks to adjust for confounding in non-tabular data.

The paper analyzes reinforcement learning methods for estimating weights and quality functions with fast convergence rates.

New method extends fitted Q-evaluation for distributional off-policy reinforcement learning.

RandQL is a new model-free algorithm for MDPs with a novel learning rate randomization approach.

Hybrid RL algorithms improve offline and online RL in linear MDPs.

Proposes a method to enhance exploration in RL using temporal difference uncertainties.

A new algorithm for deep Q-learning with robustness to state transition uncertainty.

DML addresses biases in machine learning by estimating nuisance functions.

All-goals updating exploits the off-policy nature of Q-learning to update all possible goals an agent could have from each transition in the world, and was introduced into Reinforcement Learning (RL) by Kaelbling (1993). In prior work this was mostly explored in small-state RL problems that allowed tabular representati…

Differential privacy formalises privacy-preserving mechanisms that provide access to a database. We pose the question of whether Bayesian inference itself can be used directly to provide private access to data, with no modification. The answer is affirmative: under certain conditions on the prior, sampling from the pos…

Personalized federated learning improves model accuracy and fairness by leveraging shared representations and local memorization.

Geometric framework detects outliers in high-dimensional data.

Satellite imagery helps adjust for unobserved confounders in observational studies.

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.

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.

New method learns robust representations by modeling environment variation.

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

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…

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…

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…

New algorithm guarantees domain generalization with few environments.

NetHack Learning Environment (NLE) tests RL algorithms, offering scalable, complex, and challenging gameplay.

This paper introduces Dex, a reinforcement learning environment toolkit specialized for training and evaluation of continual learning methods as well as general reinforcement learning problems. We also present the novel continual learning method of incremental learning, where a challenging environment is solved using o…

The paper formalizes robot environments using topological concepts.

New algorithm improves model generalization in structured biomedical domains.

An agent explores indefinitely in an environment with unlimited rewards.