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
29 papers · 2 filters
Learning Sparse Representations Incrementally in Deep Reinforcement Learning
J. Fernando Hernandez-Garcia, Richard S. Sutton
Sparse representations have been shown to be useful in deep reinforcement learning for mitigating catastrophic interference and improving the performance of agents in terms of cumu…
Winning the Lottery with Continuous Sparsification
Pedro Savarese, Hugo Silva, Michael Maire
The search for efficient, sparse deep neural network models is most prominently performed by pruning: training a dense, overparameterized network and removing parameters, usually v…
TreeGen: A Tree-Based Transformer Architecture for Code Generation
Zeyu Sun, Qihao Zhu, Yingfei Xiong +3
A code generation system generates programming language code based on an input natural language description. State-of-the-art approaches rely on neural networks for code generation…
Bounds for Approximate Regret-Matching Algorithms
Ryan D'Orazio, Dustin Morrill, James R. Wright
A dominant approach to solving large imperfect-information games is Counterfactural Regret Minimization (CFR). In CFR, many regret minimization problems are combined to solve the g…
Efficient decorrelation of features using Gramian in Reinforcement Learning
Borislav Mavrin, Daniel Graves, Alan Chan
Learning good representations is a long standing problem in reinforcement learning (RL). One of the conventional ways to achieve this goal in the supervised setting is through regu…
Variance Reduced Advantage Estimation with Hindsight Credit Assignment
Kenny Young
Hindsight Credit Assignment (HCA) refers to a recently proposed family of methods for producing more efficient credit assignment in reinforcement learning. These methods work by ex…
Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online
Yangchen Pan, Kirby Banman, Martha White
Recent work has shown that sparse representations -- where only a small percentage of units are active -- can significantly reduce interference. Those works, however, relied on rel…
Is Fast Adaptation All You Need?
Khurram Javed, Hengshuai Yao, Martha White
Gradient-based meta-learning has proven to be highly effective at learning model initializations, representations, and update rules that allow fast adaptation from a few samples. T…
Domain Aggregation Networks for Multi-Source Domain Adaptation
Junfeng Wen, Russell Greiner, Dale Schuurmans
In many real-world applications, we want to exploit multiple source datasets of similar tasks to learn a model for a different but related target dataset -- e.g., recognizing chara…
MarlRank: Multi-agent Reinforced Learning to Rank
Shihao Zou, Zhonghua Li, Mohammad Akbari +2
When estimating the relevancy between a query and a document, ranking models largely neglect the mutual information among documents. A common wisdom is that if two documents are si…
The Learning of Fuzzy Cognitive Maps With Noisy Data: A Rapid and Robust Learning Method With Maximum Entropy
Guoliang Feng, Wei Lu, Witold Pedrycz +2
Numerous learning methods for fuzzy cognitive maps (FCMs), such as the Hebbian-based and the population-based learning methods, have been developed for modeling and simulating dyna…
Learning Privately over Distributed Features: An ADMM Sharing Approach
Yaochen Hu, Peng Liu, Linglong Kong +1
Distributed machine learning has been widely studied in order to handle exploding amount of data. In this paper, we study an important yet less visited distributed learning problem…
Incrementally Learning Functions of the Return
Brendan Bennett, Wesley Chung, Muhammad Zaheer +1
Temporal difference methods enable efficient estimation of value functions in reinforcement learning in an incremental fashion, and are of broader interest because they correspond…
Hill Climbing on Value Estimates for Search-control in Dyna
Yangchen Pan, Hengshuai Yao, Amir-massoud Farahmand +1
Dyna is an architecture for model-based reinforcement learning (RL), where simulated experience from a model is used to update policies or value functions. A key component of Dyna…
Exploration by Optimisation in Partial Monitoring
Tor Lattimore, Csaba Szepesvari
We provide a simple and efficient algorithm for adversarial -action -outcome non-degenerate locally observable partial monitoring game for which the -round minimax regret…
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study
Cam Linke, Nadia M. Ady, Martha White +2
Learning about many things can provide numerous benefits to a reinforcement learning system. For example, learning many auxiliary value functions, in addition to optimizing the env…
Importance Resampling for Off-policy Prediction
Matthew Schlegel, Wesley Chung, Daniel Graves +2
Importance sampling (IS) is a common reweighting strategy for off-policy prediction in reinforcement learning. While it is consistent and unbiased, it can result in high variance u…
Neural Replicator Dynamics
Daniel Hennes, Dustin Morrill, Shayegan Omidshafiei +8
Policy gradient and actor-critic algorithms form the basis of many commonly used training techniques in deep reinforcement learning. Using these algorithms in multiagent environmen…
Distributional Reinforcement Learning for Efficient Exploration
Borislav Mavrin, Shangtong Zhang, Hengshuai Yao +3
In distributional reinforcement learning (RL), the estimated distribution of value function models both the parametric and intrinsic uncertainties. We propose a novel and efficient…
Interpretable PID Parameter Tuning for Control Engineering using General Dynamic Neural Networks: An Extensive Comparison
Johannes Günther, Elias Reichensdörfer, Patrick M. Pilarski +1
Modern automation systems rely on closed loop control, wherein a controller interacts with a controlled process, based on observations. These systems are increasingly complex, yet…
Meta-Learning Representations for Continual Learning
Khurram Javed, Martha White
A continual learning agent should be able to build on top of existing knowledge to learn on new data quickly while minimizing forgetting. Current intelligent systems based on neura…
When is a Prediction Knowledge?
Alex Kearney, Patrick M. Pilarski
Within Reinforcement Learning, there is a growing collection of research which aims to express all of an agent's knowledge of the world through predictions about sensation, behavio…
Exponential Family Estimation via Adversarial Dynamics Embedding
Bo Dai, Zhen Liu, Hanjun Dai +4
We present an efficient algorithm for maximum likelihood estimation (MLE) of exponential family models, with a general parametrization of the energy function that includes neural n…
Planning with Expectation Models
Yi Wan, Zaheer Abbas, Adam White +2
Distribution and sample models are two popular model choices in model-based reinforcement learning (MBRL). However, learning these models can be intractable, particularly when the…