arXiv research

Proposes a new model for predicting future motion of road actors in autonomous vehicles.

Model visualizes and analyzes multilayer networks in a latent space.

In a dynamic social or biological environment, the interactions between the actors can undergo large and systematic changes. In this paper we propose a model-based approach to analyze what we will refer to as the dynamic tomography of such time-evolving networks. Our approach offers an intuitive but powerful tool to in…

Actors in realistic social networks play not one but a number of diverse roles depending on whom they interact with, and a large number of such role-specific interactions collectively determine social communities and their organizations. Methods for analyzing social networks should capture these multi-faceted role-spec…

Optimizes social interactions for profit, people, and planet using mathematical models.

Framework detects influential actors in disinformation networks.

Network analysis helps prevent money laundering by identifying risky clients and suspicious clusters.

Networks or graphs can easily represent a diverse set of data sources that are characterized by interacting units or actors. Social networks, representing people who communicate with each other, are one example. Communities or clusters of highly connected actors form an essential feature in the structure of several emp…

New analysis shows rational actors will deploy AGI despite negative social value due to catastrophic risk.

New framework for network regression models accounting for community structure.

Study shows financial literacy, social capital, and financial tech positively impact financial inclusion of Indonesian students.

To achieve the ambitious aims of the Paris climate agreement, the majority of fossil-fuel reserves needs to remain underground. As current national government commitments to mitigate greenhouse gas emissions are insufficient by far, actors such as institutional and private investors and the social movement on divestmen…

DASC combines social media and car sensors to improve disaster response.

What is the role of social interactions in the creation of price bubbles? Answering this question requires obtaining collective behavioural traces generated by the activity of a large number of actors. Digital currencies offer a unique possibility to measure socio-economic signals from such digital traces. Here, we foc…

The blockchain technology promises to transform finance, money and even governments. However, analyses of blockchain applicability and robustness typically focus on isolated systems whose actors contribute mainly by running the consensus algorithm. Here, we highlight the importance of considering trustless platforms wi…

Fast algorithm for analyzing huge social networks.

A plethora of networks is being collected in a growing number of fields, including disease transmission, international relations, social interactions, and others. As data streams continue to grow, the complexity associated with these highly multidimensional connectivity data presents novel challenges. In this paper, we…

Actor-critic methods solve reinforcement learning problems by updating a parameterized policy known as an actor in a direction that increases an estimate of the expected return known as a critic. However, existing actor-critic methods only use values or gradients of the critic to update the policy parameter. In this pa…

The paper analyzes an actor-critic algorithm with target networks for deep reinforcement learning.

Single-timescale actor-critic finds globally optimal policy.

New PAC-Bayesian approach stabilizes actor-critic learning.

Actor-critic methods can achieve incredible performance on difficult reinforcement learning problems, but they are also prone to instability. This is partly due to the interaction between the actor and critic during learning, e.g., an inaccurate step taken by one of them might adversely affect the other and destabilize…

FORK improves model-free reinforcement learning performance.

Smaller actor-critic models lead to performance degradation and overfitting, highlighting the critic's role in value underestimation.

New analysis shows actor-critic method converges efficiently in practical settings.

This work analyzes actor-critic methods for faster convergence.

Despite the empirical success of the actor-critic algorithm, its theoretical understanding lags behind. In a broader context, actor-critic can be viewed as an online alternating update algorithm for bilevel optimization, whose convergence is known to be fragile. To understand the instability of actor-critic, we focus o…

Actor-critic algorithms converge to an ODE as data samples change dynamically.

We introduce an online tensor decomposition based approach for two latent variable modeling problems namely, (1) community detection, in which we learn the latent communities that the social actors in social networks belong to, and (2) topic modeling, in which we infer hidden topics of text articles. We consider decomp…

AGAC uses an adversary to enhance exploration in complex tasks.

Many policy gradient methods are variants of Actor-Critic (AC), where a value function (critic) is learned to facilitate updating the parameterized policy (actor). The update to the actor involves a log-likelihood update weighted by the action-values, with the addition of entropy regularization for soft variants. In th…

Neural networks trained with actor-critic algorithms converge to ODEs under weak convergence analysis.

SAVO actor improves reinforcement learning by avoiding local optima in complex Q-functions.

Traffic actors' future motion predicted using a hybrid graph model.

In this paper we propose a hybrid architecture of actor-critic algorithms for reinforcement learning in parameterized action space, which consists of multiple parallel sub-actor networks to decompose the structured action space into simpler action spaces along with a critic network to guide the training of all sub-acto…

Actor-Critic method achieves optimal regret for unichain MDPs.

TT-DAC-PS: A deterministic actor-critic approach for optimal trade execution

Proposes a value-based method for continuous control without an actor.

Optimistic actor-critic tackles linear MDPs with parametric policies.

Paper introduces a meta-critic for accelerating off-policy actor-critic learning.

ESAC improves reinforcement learning by lookahead and intuition.

We present an actor-critic framework for MDPs where the objective is the variance-adjusted expected return. Our critic uses linear function approximation, and we extend the concept of compatible features to the variance-adjusted setting. We present an episodic actor-critic algorithm and show that it converges almost su…

We report a data mining pipeline and subsequent analysis to understand the core periphery power structure created in three national newspapers in Bangladesh, as depicted by statements made by people appearing in news. Statements made by one actor about another actor can be considered a form of public conversation. Name…

WAVE improves stability in reinforcement learning by adaptively weighting critic's loss.

Study uses actor-critic method for continuous-time mean-field control with entropy regularisation.

A simple guide to understanding hierarchical causality in complex systems.

We model messaging activities as a hierarchical doubly stochastic point process with three main levels, and develop an iterative algorithm for inferring actors' relative latent positions from a stream of messaging activity data. Each of the message-exchanging actors is modeled as a process in a latent space. The actors…

A new method improves actor-critic RL by integrating HMC, enhancing policy distribution and exploration.