arXiv research

Paper proves Pareto efficient insurance for multiple entities.

Meta-learners estimate CATE from multiple environments with partial identification.

Detect hidden confounding in observational data using multiple environments.

New reward function improves GAIL performance in task-based 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…

ICIL learns policies invariant to multiple environments, improving generalization.

New method uses unlabeled data to improve model robustness across different environments.

Recent advances in reinforcement learning have proved that given an environment we can learn to perform a task in that environment if we have access to some form of a reward function (dense, sparse or derived from IRL). But most of the algorithms focus on learning a single best policy to perform a given set of tasks. I…

New methods improve prediction intervals across multiple environments.

Hierarchical Reinforcement Learning (HRL) exploits temporally extended actions, or options, to make decisions from a higher-dimensional perspective to alleviate the sparse reward problem, one of the most challenging problems in reinforcement learning. The majority of existing HRL algorithms require either significant m…

Study shows tractable generalization in RL is impossible but possible with Strong Proximity.

Improves reinforcement learning agent's scene-specific value function.

In this paper, we propose a model for the Environment Sound Classification Task (ESC) that consists of multiple feature channels given as input to a Deep Convolutional Neural Network (CNN) with Attention mechanism. The novelty of the paper lies in using multiple feature channels consisting of Mel-Frequency Cepstral Coe…

Deep reinforcement learning (RL) has been successfully applied to a variety of game-like environments. However, the application of deep RL to visual navigation with realistic environments is a challenging task. We propose a novel learning architecture capable of navigating an agent, e.g. a mobile robot, to a target giv…

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…

RAMEN corrects observational data biases for multiple environments.

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

Most existing fingerprints-based indoor localization approaches are based on some single fingerprints, such as received signal strength (RSS), channel impulse response (CIR), and signal subspace. However, the localization accuracy obtained by the single fingerprint approach is rather susceptible to the changing environ…

We propose a novel approach to address one aspect of the non-stationarity problem in multi-agent reinforcement learning (RL), where the other agents may alter their policies due to environment changes during execution. This violates the Markov assumption that governs most single-agent RL methods and is one of the key c…

A new method for learning policies in multiple environments.

UAED discovers adaptive environments for robust learning.

Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal. However, most existing approaches are not applicable in multi-agent settings due to the existence of multiple (Nash) equilibria and non-stationary environments. We propose a new framework for multi-…

Assisted by neural networks, reinforcement learning agents have been able to solve increasingly complex tasks over the last years. The simulation environment in which the agents interact is an essential component in any reinforcement learning problem. The environment simulates the dynamics of the agents' world and henc…

ReOPD uses pre-collected teacher trajectories to distill knowledge from multi-turn interactions.

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

New algorithms detect and react to multiple change points in online learning.

The capability to learn and adapt to changes in the driving environment is crucial for developing autonomous driving systems that are scalable beyond geo-fenced operational design domains. Deep Reinforcement Learning (RL) provides a promising and scalable framework for developing adaptive learning based solutions. Deep…

New method identifies causal graphs with limited data and noise.

Introduces Conditional Action Trees to simplify RL action spaces.

Study identifies latent variables and causal relationships from multiple environments.

CHEER boosts poor models using rich model knowledge.

New method learns robust representations by modeling environment variation.

Deep learning has enabled traditional reinforcement learning methods to deal with high-dimensional problems. However, one of the disadvantages of deep reinforcement learning methods is the limited exploration capacity of learning agents. In this paper, we introduce an approach that integrates human strategies to increa…

New algorithm improves model generalization in structured biomedical domains.

Study on estimating causal effects with limited data and multiple environments.

Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability. However, existing approaches suffer from structural limitations and optimization difficulties for common environments with multiple dynamic objects. In this paper, we present a novel self-s…

When agents interact with a complex environment, they must form and maintain beliefs about the relevant aspects of that environment. We propose a way to efficiently train expressive generative models in complex environments. We show that a predictive algorithm with an expressive generative model can form stable belief-…

Two environments are enough to infer causal graphs and counterfactuals.

A new method for estimating joint value functions in multi-scene reinforcement learning.

The paper develops methods to identify stable associations across multiple studies.

While machine learning is rapidly being developed and deployed in health settings such as influenza prediction, there are critical challenges in using data from one environment in another due to variability in features; even within disease labels there can be differences (e.g. "fever" may mean something different repor…

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

Bayesian Invariant Prediction models stable features from multi-environment data.

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

Reproducibility in reinforcement learning is challenging: uncontrolled stochasticity from many sources, such as the learning algorithm, the learned policy, and the environment itself have led researchers to report the performance of learned agents using aggregate metrics of performance over multiple random seeds for a …

We present MDP Playground, a testbed for Reinforcement Learning (RL) agents with dimensions of hardness that can be controlled independently to challenge agents in different ways and obtain varying degrees of hardness in toy and complex RL environments. We consider and allow control over a wide variety of dimensions, i…

This paper uses probability tensors for efficient path planning in complex scenarios.

New algorithm for reinforcement learning in uncertain environments with unknown thresholds.