Deep Reinforcement Learning Papers

A list of recent papers regarding deep reinforcement learning. 
The papers are organized based on manually-defined bookmarks. 
They are sorted by time to see the recent papers first. 
Any suggestions and pull requests are welcome.

Bookmarks

• All Papers
• Value Function Approximation
• Policy Gradient
• Discrete Control
• Continuous Control
• Text Domain
• Visual Domain
• Robotics
• Games
• Monte-Carlo Tree Search
• Inverse Reinforcement Learning
• Improving Exploration
• Transfer Learning
• Multi-Agent

All Papers

• Continuous Deep Q-Learning with Model-based Acceleration, Shixiang Gu et al., arXiv, 2016.
• Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
• Value Iteration Networks, A. Tamar et al., arXiv, 2016.
• Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv, 2016.
• Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
• Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
• Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
• Memory-based control with recurrent neural networks, N. Heess et al., NIPS Workshop, 2015.
• How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
• Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.
• Strategic Dialogue Management via Deep Reinforcement Learning, H. Cuayáhuitl et al., NIPS Workshop, 2015.
• MazeBase: A Sandbox for Learning from Games, S. Sukhbaatar et al., arXiv, 2016.
• Learning Simple Algorithms from Examples, W. Zaremba et al., arXiv, 2015.
• Dueling Network Architectures for Deep Reinforcement Learning, Z. Wang et al., arXiv, 2015.
• Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR, 2016.
• Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR, 2016.
• Policy Distillation, A. A. Rusu et at., ICLR, 2016.
• Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
• Deep Reinforcement Learning with an Action Space Defined by Natural Language, J. He et al., arXiv, 2015.
• Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR, 2016.
• Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control, F. Zhang et al., arXiv, 2015.
• Generating Text with Deep Reinforcement Learning, H. Guo, arXiv, 2015.
• ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources, J. Rajendran et al., arXiv, 2015.
• Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning, S. Mohamed and D. J. Rezende,arXiv, 2015.
• Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
• Recurrent Reinforcement Learning: A Hybrid Approach, X. Li et al., arXiv, 2015.
• Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
• Language Understanding for Text-based Games Using Deep Reinforcement Learning, K. Narasimhan et al., EMNLP, 2015.
• Giraffe: Using Deep Reinforcement Learning to Play Chess, M. Lai, arXiv, 2015.
• Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
• Learning Continuous Control Policies by Stochastic Value Gradients, N. Heess et al., NIPS, 2015.
• Learning Deep Neural Network Policies with Continuous Memory States, M. Zhang et al., arXiv, 2015.
• Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.
• Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences, H. Mei et al., arXiv, 2015.
• Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.
• Maximum Entropy Deep Inverse Reinforcement Learning, M. Wulfmeier et al., arXiv, 2015.
• High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR, 2016.
• End-to-End Training of Deep Visuomotor Policies, S. Levine et al., arXiv, 2015.
• DeepMPC: Learning Deep Latent Features for Model Predictive Control, I. Lenz, et al., RSS, 2015.
• Universal Value Function Approximators, T. Schaul et al., ICML, 2015.
• Deterministic Policy Gradient Algorithms, D. Silver et al., ICML, 2015.
• Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
• Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.
• Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
• Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, X. Guo et al., NIPS, 2014.
• Playing Atari with Deep Reinforcement Learning, V. Mnih et al., NIPS Workshop, 2013.

Value Function Approximation

• Continuous Deep Q-Learning with Model-based Acceleration, Shixiang Gu et al., arXiv, 2016.
• Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
• Value Iteration Networks, A. Tamar et al., arXiv, 2016.
• Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv, 2016.
• Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
• Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
• Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
• How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
• Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.
• Strategic Dialogue Management via Deep Reinforcement Learning, H. Cuayáhuitl et al., NIPS Workshop, 2015.
• Learning Simple Algorithms from Examples, W. Zaremba et al., arXiv, 2015.
• Dueling Network Architectures for Deep Reinforcement Learning, Z. Wang et al., arXiv, 2015.
• Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
• Deep Reinforcement Learning with an Action Space Defined by Natural Language, J. He et al., arXiv, 2015.
• Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR, 2016.
• Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control, F. Zhang et al., arXiv, 2015.
• Generating Text with Deep Reinforcement Learning, H. Guo, arXiv, 2015.
• Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
• Recurrent Reinforcement Learning: A Hybrid Approach, X. Li et al., arXiv, 2015.
• Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
• Language Understanding for Text-based Games Using Deep Reinforcement Learning, K. Narasimhan et al., EMNLP, 2015.
• Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
• Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.
• Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.
• Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
• Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
• Playing Atari with Deep Reinforcement Learning, V. Mnih et al., NIPS Workshop, 2013.

Policy Gradient

• Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
• Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
• Memory-based control with recurrent neural networks, N. Heess et al., NIPS Workshop, 2015.
• MazeBase: A Sandbox for Learning from Games, S. Sukhbaatar et al., arXiv, 2016.
• ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources, J. Rajendran et al., arXiv, 2015.
• Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
• Learning Continuous Control Policies by Stochastic Value Gradients, N. Heess et al., NIPS, 2015.
• High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR, 2016.
• End-to-End Training of Deep Visuomotor Policies, S. Levine et al., arXiv, 2015.
• Deterministic Policy Gradient Algorithms, D. Silver et al., ICML, 2015.
• Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.

Discrete Control

• Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
• Value Iteration Networks, A. Tamar et al., arXiv, 2016.
• Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv, 2016.
• Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
• Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
• Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
• How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
• Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.
• Strategic Dialogue Management via Deep Reinforcement Learning, H. Cuayáhuitl et al., NIPS Workshop, 2015.
• Learning Simple Algorithms from Examples, W. Zaremba et al., arXiv, 2015.
• Dueling Network Architectures for Deep Reinforcement Learning, Z. Wang et al., arXiv, 2015.
• Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR, 2016.
• Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR, 2016.
• Policy Distillation, A. A. Rusu et at., ICLR, 2016.
• Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
• Deep Reinforcement Learning with an Action Space Defined by Natural Language, J. He et al., arXiv, 2015.
• Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR, 2016.
• Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control, F. Zhang et al., arXiv, 2015.
• Generating Text with Deep Reinforcement Learning, H. Guo, arXiv, 2015.
• ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources, J. Rajendran et al., arXiv, 2015.
• Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning, S. Mohamed and D. J. Rezende,arXiv, 2015.
• Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
• Recurrent Reinforcement Learning: A Hybrid Approach, X. Li et al., arXiv, 2015.
• Language Understanding for Text-based Games Using Deep Reinforcement Learning, K. Narasimhan et al., EMNLP, 2015.
• Giraffe: Using Deep Reinforcement Learning to Play Chess, M. Lai, arXiv, 2015.
• Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
• Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.
• Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences, H. Mei et al., arXiv, 2015.
• Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.
• Universal Value Function Approximators, T. Schaul et al., ICML, 2015.
• Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
• Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
• Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, X. Guo et al., NIPS, 2014.
• Playing Atari with Deep Reinforcement Learning, V. Mnih et al., NIPS Workshop, 2013.

Continuous Control

• Continuous Deep Q-Learning with Model-based Acceleration, Shixiang Gu et al., arXiv, 2016.
• Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
• Memory-based control with recurrent neural networks, N. Heess et al., NIPS Workshop, 2015.
• Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning, S. Mohamed and D. J. Rezende,arXiv, 2015.
• Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
• Learning Continuous Control Policies by Stochastic Value Gradients, N. Heess et al., NIPS, 2015.
• Learning Deep Neural Network Policies with Continuous Memory States, M. Zhang et al., arXiv, 2015.
• High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR, 2016.
• End-to-End Training of Deep Visuomotor Policies, S. Levine et al., arXiv, 2015.
• DeepMPC: Learning Deep Latent Features for Model Predictive Control, I. Lenz, et al., RSS, 2015.
• Deterministic Policy Gradient Algorithms, D. Silver et al., ICML, 2015.
• Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.

Text Domain

• Strategic Dialogue Management via Deep Reinforcement Learning, H. Cuayáhuitl et al., NIPS Workshop, 2015.
• MazeBase: A Sandbox for Learning from Games, S. Sukhbaatar et al., arXiv, 2016.
• Deep Reinforcement Learning with an Action Space Defined by Natural Language, J. He et al., arXiv, 2015.
• Generating Text with Deep Reinforcement Learning, H. Guo, arXiv, 2015.
• Language Understanding for Text-based Games Using Deep Reinforcement Learning, K. Narasimhan et al., EMNLP, 2015.
• Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequences, H. Mei et al., arXiv, 2015.

Visual Domain

• Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
• Value Iteration Networks, A. Tamar et al., arXiv, 2016.
• Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
• Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
• Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
• Memory-based control with recurrent neural networks, N. Heess et al., NIPS Workshop, 2015.
• How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
• Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.
• Dueling Network Architectures for Deep Reinforcement Learning, Z. Wang et al., arXiv, 2015.
• Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR, 2016.
• Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR, 2016.
• Policy Distillation, A. A. Rusu et at., ICLR, 2016.
• Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
• Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR, 2016.
• Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control, F. Zhang et al., arXiv, 2015.
• Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning, S. Mohamed and D. J. Rezende,arXiv, 2015.
• Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
• Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
• Giraffe: Using Deep Reinforcement Learning to Play Chess, M. Lai, arXiv, 2015.
• Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
• Learning Continuous Control Policies by Stochastic Value Gradients, N. Heess et al., NIPS, 2015.
• Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.
• Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.
• High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR, 2016.
• End-to-End Training of Deep Visuomotor Policies, S. Levine et al., arXiv, 2015.
• Universal Value Function Approximators, T. Schaul et al., ICML, 2015.
• Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
• Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.
• Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
• Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, X. Guo et al., NIPS, 2014.
• Playing Atari with Deep Reinforcement Learning, V. Mnih et al., NIPS Workshop, 2013.

Robotics

• Continuous Deep Q-Learning with Model-based Acceleration, Shixiang Gu et al., arXiv, 2016.
• Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
• Memory-based control with recurrent neural networks, N. Heess et al., NIPS Workshop, 2015.
• Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control, F. Zhang et al., arXiv, 2015.
• Learning Continuous Control Policies by Stochastic Value Gradients, N. Heess et al., NIPS, 2015.
• Learning Deep Neural Network Policies with Continuous Memory States, M. Zhang et al., arXiv, 2015.
• High-Dimensional Continuous Control Using Generalized Advantage Estimation, J. Schulman et al., ICLR, 2016.
• End-to-End Training of Deep Visuomotor Policies, S. Levine et al., arXiv, 2015.
• DeepMPC: Learning Deep Latent Features for Model Predictive Control, I. Lenz, et al., RSS, 2015.
• Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.

Games

• Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
• Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv, 2016.
• Asynchronous Methods for Deep Reinforcement Learning, V. Mnih et al., arXiv, 2016.
• Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
• Increasing the Action Gap: New Operators for Reinforcement Learning, M. G. Bellemare et al., AAAI, 2016.
• How to Discount Deep Reinforcement Learning: Towards New Dynamic Strategies, V. François-Lavet et al., NIPS Workshop, 2015.
• Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.
• MazeBase: A Sandbox for Learning from Games, S. Sukhbaatar et al., arXiv, 2016.
• Dueling Network Architectures for Deep Reinforcement Learning, Z. Wang et al., arXiv, 2015.
• Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR, 2016.
• Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR, 2016.
• Policy Distillation, A. A. Rusu et at., ICLR, 2016.
• Prioritized Experience Replay, T. Schaul et al., ICLR, 2016.
• Deep Reinforcement Learning with an Action Space Defined by Natural Language, J. He et al., arXiv, 2015.
• Deep Reinforcement Learning in Parameterized Action Space, M. Hausknecht et al., ICLR, 2016.
• Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning, S. Mohamed and D. J. Rezende,arXiv, 2015.
• Deep Reinforcement Learning with Double Q-learning, H. van Hasselt et al., arXiv, 2015.
• Continuous control with deep reinforcement learning, T. P. Lillicrap et al., ICLR, 2016.
• Language Understanding for Text-based Games Using Deep Reinforcement Learning, K. Narasimhan et al., EMNLP, 2015.
• Giraffe: Using Deep Reinforcement Learning to Play Chess, M. Lai, arXiv, 2015.
• Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
• Deep Recurrent Q-Learning for Partially Observable MDPs, M. Hausknecht and P. Stone, arXiv, 2015.
• Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.
• Universal Value Function Approximators, T. Schaul et al., ICML, 2015.
• Massively Parallel Methods for Deep Reinforcement Learning, A. Nair et al., ICML Workshop, 2015.
• Trust Region Policy Optimization, J. Schulman et al., ICML, 2015.
• Human-level control through deep reinforcement learning, V. Mnih et al., Nature, 2015.
• Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, X. Guo et al., NIPS, 2014.
• Playing Atari with Deep Reinforcement Learning, V. Mnih et al., NIPS Workshop, 2013.

Monte-Carlo Tree Search

• Mastering the game of Go with deep neural networks and tree search, D. Silver et al., Nature, 2016.
• Better Computer Go Player with Neural Network and Long-term Prediction, Y. Tian et al., ICLR, 2016.
• Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning, X. Guo et al., NIPS, 2014.

Inverse Reinforcement Learning

• Maximum Entropy Deep Inverse Reinforcement Learning, M. Wulfmeier et al., arXiv, 2015.

Transfer Learning

• Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning, E. Parisotto, et al., ICLR, 2016.
• Policy Distillation, A. A. Rusu et at., ICLR, 2016.
• ADAAPT: A Deep Architecture for Adaptive Policy Transfer from Multiple Sources, J. Rajendran et al., arXiv, 2015.
• Universal Value Function Approximators, T. Schaul et al., ICML, 2015.

Improving Exploration

• Deep Exploration via Bootstrapped DQN, I. Osband et al., arXiv, 2016.
• Action-Conditional Video Prediction using Deep Networks in Atari Games, J. Oh et al., NIPS, 2015.
• Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models, B. C. Stadie et al., arXiv, 2015.

Multi-Agent

• Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks, J. N. Foerster et al., arXiv, 2016.
• Multiagent Cooperation and Competition with Deep Reinforcement Learning, A. Tampuu et al., arXiv, 2015.

Best Last Month

Linear Algebra and Differential Equation

Information industry

by wittx

Loss Function 损失函数

Computer software and hardware

by wittx

一起教育科技纳斯达克挂牌上市

Information industry

by wittx

机器人运动控制算法5

Mechanical electromechanical

by wittx

资产配置 MPT

Information industry

by wittx

DeepMind最新研究NFNet：抛弃归一化，深度学习模型

Information industry

by show

Introduction to Probability

Information industry

by wittx

Three dimensional architected thermoelectric devices with high toughness and power conversion effici

Information industry

by wittx

集成电路简要概述

Information industry

by wittx

量化投资策略与Smart Beta

Information industry

by wittx