Deep reinforcement learning (DRL) algorithms and evolution strategies (ES) have been applied to various tasks, showing excellent performances. These have the opposite properties, with DRL having good sample efficiency and poor stability, while ES being vice versa. Recently, there have been attempts to combine these algorithms, but these methods fully rely on synchronous update scheme, making it not ideal to maximize the benefits of the parallelism in ES. To solve this challenge, asynchronous update scheme was introduced, which is capable of good time-efficiency and diverse policy exploration. In this paper, we introduce an Asynchronous Evolution Strategy-Reinforcement Learning (AES-RL) that maximizes the parallel efficiency of ES and integrates it with policy gradient methods. Specifically, we propose 1) a novel framework to merge ES and DRL asynchronously and 2) various asynchronous update methods that can take all advantages of asynchronism, ES, and DRL, which are exploration and time efficiency, stability, and sample efficiency, respectively. The proposed framework and update methods are evaluated in continuous control benchmark work, showing superior performance as well as time efficiency compared to the previous methods.
Southern University of Science and Technology（南方科技大学）；NetEase Games AI Lab（网易游戏人工智能实验室）
Despite the emerging progress of integrating evolutionary computation into reinforcement learning, the absence of a high-performance platform endowing composability and massive parallelism causes non-trivial difficulties for research and applications related to asynchronous commercial games. Here we introduce Lamarckian - an open-source platform featuring support for evolutionary reinforcement learning scalable to distributed computing resources. To improve the training speed and data efficiency, Lamarckian adopts optimized communication methods and an asynchronous evolutionary reinforcement l…
Optimizing Search Advertising Strategies: Integrating Reinforcement Learning with Generalized Second-Price Auctions for Enhanced Ad Ranking and Bidding
Columbia University（哥伦比亚大学）；University of Michigan（密歇根大学）；University of California, Irvine（加利福尼亚大学尔湾分校）
Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities
National Defense University（国防大学）；University of Kent（肯特大学）；Beijing Jiaotong University（北京交通大学）；Nanyang Technological University（南洋理工大学）；Qatar University（卡塔尔大学）；Beihang University（北京航空航天大学）；National University of Singapore（新加坡国立大学）；University of Alberta（阿尔伯塔大学）；Polish Academy of Sciences（波兰科学院）；Indian Statistical Institute（印度统计学院）；Kyungpook National University（庆北国立大学）
College of Intelligent Science and Engineering, Harbin Engineering University（哈尔滨工程大学智能科学与工程学院）；School of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学与技术学院）；Silesian University of Technology（西里西亚工业大学）；University of Alberta（阿尔伯塔大学）；Constructor University（康斯特鲁克托大学）；Istinye University（伊斯蒂涅大学）
Oak Ridge National Laboratory（橡树岭国家实验室）；Georgia Institute of Technology（佐治亚理工学院）；University of Tennessee, Knoxville（田纳西大学诺克斯维尔分校）
This paper presents some of the current challenges in designing deep learning artificial intelligence (AI) and integrating it with traditional high-performance computing (HPC) simulations. We evaluate existing packages for their ability to run deep learning models and applications on large-scale HPC systems efficiently, identify challenges, and propose new asynchronous parallelization and optimization techniques for current large-scale heterogeneous systems and upcoming exascale systems. These developments, along with existing HPC AI software capabilities, have been integrated into MagmaDNN, a…
Toyota Technological Institute at Chicago（芝加哥丰田工业大学）
Protein contacts contain important information for protein structure and functional study, but contact prediction from sequence remains very challenging. Both evolutionary coupling (EC) analysis and supervised machine learning methods are developed to predict contacts, making use of different types of information, respectively. This paper presents a group graphical lasso (GGL) method for contact prediction that integrates joint multi-family EC analysis and supervised learning. Different from existing single-family EC analysis that uses residue co-evolution information in only the target protei…
Integrating Dynamical Systems Learning with Foundational Models: A Meta-Evolutionary AI Framework for Clinical Trials
NetraMark Corp.（NetraMark公司）；Queen’s University（女王大学）；Tandem Centre for Pharmacogenetics（药基因学中心）；UC San Diego（圣地亚哥大学）；University of Miami（迈阿密大学）；University of Modena and Reggio Emilia（摩德纳和雷焦艾米利亚大学）
In this paper, a novel two-branch neural network model structure is proposed for multimodal emotion recognition, which consists of a time synchronous branch (TSB) and a time asynchronous branch (TAB). To capture correlations between each word and its acoustic realisation, the TSB combines speech and text modalities at each input window frame and then does pooling across time to form a single embedding vector. The TAB, by contrast, provides cross-utterance information by integrating sentence text embeddings from a number of context utterances into another embedding vector. The final emotion cla…
Allen Discovery Center at Tufts University（塔夫茨大学艾伦发现中心）；Institute for Theoretical Physics, TU Wien（维也纳技术大学理论物理研究所）；Wyss Institute for Biologically Inspired Engineering at Harvard University（哈佛大学威斯生物启发工程研究所）
Tsinghua University（清华大学）；Tencent AI Lab（腾讯人工智能实验室）
Modern distributed training of machine learning models suffers from high communication overhead for synchronizing stochastic gradients and model parameters. In this paper, to reduce the communication complexity, we propose \emph{double quantization}, a general scheme for quantizing both model parameters and gradients. Three communication-efficient algorithms are proposed under this general scheme. Specifically, (i) we propose a low-precision algorithm AsyLPG with asynchronous parallelism, (ii) we explore integrating gradient sparsification with double quantization and develop Sparse-AsyLPG, (i…
