Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of BonnDE1.5k papers
- University of OxfordGB1.5k papers
- Lawrence Berkeley National LaboratoryUS1.5k papers
- Michigan State UniversityUS1.5k papers
- Stony Brook UniversityUS1.5k papers
- University of WuppertalDE1.5k papers
- The Ohio State UniversityUS1.5k papers
- Centre National de la Recherche ScientifiqueFR1.5k papers
- Stockholm UniversitySE1.4k papers
- Johannes Gutenberg University MainzDE1.4k papers
- Uppsala UniversitySE1.4k papers
- University of ManchesterGB1.4k papers
16 papers · 2 filters
MMA Training: Direct Input Space Margin Maximization through Adversarial Training
Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui +1
We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundar…
The Utility of Sparse Representations for Control in Reinforcement Learning
Vincent Liu, Raksha Kumaraswamy, Lei Le +1
We investigate sparse representations for control in reinforcement learning. While these representations are widely used in computer vision, their prevalence in reinforcement learn…
QUOTA: The Quantile Option Architecture for Reinforcement Learning
Shangtong Zhang, Borislav Mavrin, Linglong Kong +2
In this paper, we propose the Quantile Option Architecture (QUOTA) for exploration based on recent advances in distributional reinforcement learning (RL). In QUOTA, decision making…
ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search
Shangtong Zhang, Hao Chen, Hengshuai Yao
In this paper, we propose an actor ensemble algorithm, named ACE, for continuous control with a deterministic policy in reinforcement learning. In ACE, we use actor ensemble (i.e.,…
Online Off-policy Prediction
Sina Ghiassian, Andrew Patterson, Martha White +2
This paper investigates the problem of online prediction learning, where learning proceeds continuously as the agent interacts with an environment. The predictions made by the agen…
An Off-policy Policy Gradient Theorem Using Emphatic Weightings
Ehsan Imani, Eric Graves, Martha White
Policy gradient methods are widely used for control in reinforcement learning, particularly for the continuous action setting. There have been a host of theoretically sound algorit…
Context-Dependent Upper-Confidence Bounds for Directed Exploration
Raksha Kumaraswamy, Matthew Schlegel, Adam White +1
Directed exploration strategies for reinforcement learning are critical for learning an optimal policy in a minimal number of interactions with the environment. Many algorithms use…
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement
Samuel Neumann, Sungsu Lim, Ajin Joseph +3
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 th…
Per-decision Multi-step Temporal Difference Learning with Control Variates
Kristopher De Asis, Richard S. Sutton
Multi-step temporal difference (TD) learning is an important approach in reinforcement learning, as it unifies one-step TD learning with Monte Carlo methods in a way where intermed…
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
Gellért Weisz, András György, Csaba Szepesvári
We consider the problem of configuring general-purpose solvers to run efficiently on problem instances drawn from an unknown distribution. The goal of the configurator is to find a…
General Value Function Networks
Matthew Schlegel, Andrew Jacobsen, Zaheer Abbas +3
State construction is important for learning in partially observable environments. A general purpose strategy for state construction is to learn the state update using a Recurrent…
Cleaning up the neighborhood: A full classification for adversarial partial monitoring
Tor Lattimore, Csaba Szepesvari
Partial monitoring is a generalization of the well-known multi-armed bandit framework where the loss is not directly observed by the learner. We complete the classification of fini…
Improving GAN Training via Binarized Representation Entropy (BRE) Regularization
Yanshuai Cao, Gavin Weiguang Ding, Kry Yik-Chau Lui +1
We propose a novel regularizer to improve the training of Generative Adversarial Networks (GANs). The motivation is that when the discriminator D spreads out its model capacity in…
TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent
Alex Kearney, Vivek Veeriah, Jaden B. Travnik +2
In this paper, we introduce a method for adapting the step-sizes of temporal difference (TD) learning. The performance of TD methods often depends on well chosen step-sizes, yet fe…
Model-Free Linear Quadratic Control via Reduction to Expert Prediction
Yasin Abbasi-Yadkori, Nevena Lazic, Csaba Szepesvari
Model-free approaches for reinforcement learning (RL) and continuous control find policies based only on past states and rewards, without fitting a model of the system dynamics. Th…
A Block-wise, Asynchronous and Distributed ADMM Algorithm for General Form Consensus Optimization
Rui Zhu, Di Niu, Zongpeng Li
Many machine learning models, including those with non-smooth regularizers, can be formulated as consensus optimization problems, which can be solved by the alternating direction m…