干货 | 深度强化学习国际顶会ICML-2019最新进展速览—论文PDF打包下载

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关注：决策智能与机器学习，学点AI干货

在国际会议上的机器学习（ICML）是一个国际学术会议上机器学习。它是机器学习和人工智能研究中高影响力的两个主要会议之一。每年的ICML中都有大量的关于强化学习的文章,其中2019总共接收强化学习论文46篇（已经是很高比例了，快接近10%），下面是本次会议文章的总结。

文章pdf版本汇总下载，本公众号回复：20190517

强化学习是一种通用的学习、预测和决策范式。RL为顺序决策问题提供了解决方法，并将其转化为顺序决策问题。RL与优化、统计学、博弈论、因果推理、序贯实验等有着深刻的联系，与近似动态规划和最优控制有着很大的重叠，在科学、工程和艺术领域有着广泛的应用。

RL最近在学术界取得了稳定的进展，如Atari游戏、AlphaGo、VisuoMotor机器人政策。RL也被应用于现实场景，如推荐系统和神经架构搜索。请参阅有关RL应用程序的最新集合。希望RL系统能够在现实世界中工作，并具有实际的好处。然而，RL存在着许多问题，如泛化、样本效率、勘探与开发困境等。因此，RL远未被广泛部署。对于RL社区来说，常见的、关键的和紧迫的问题是：RL是否有广泛的部署？问题是什么？如何解决这些问题？

方法类文章

• Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

• Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

• Quantifying Generalization in Reinforcement Learning

• Policy Certificates: Towards Accountable Reinforcement Learning

• Neural Logic Reinforcement Learning

• Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning

• Few-Shot Intent Inference via Meta-Inverse Reinforcement Learning

• Calibrated Model-Based Deep Reinforcement Learning

• Information-Theoretic Considerations in Batch Reinforcement Learning

• Taming MAML: Control variates for unbiased meta-reinforcement learning gradient estimation

• Option Discovery for Solving Sparse Reward Reinforcement Learning Problems

优化类文章

• Fingerprint Policy Optimisation for Robust Reinforcement Learning

• Collaborative Evolutionary Reinforcement Learning

• Composing Value Functions in Reinforcement Learning

• Task-Agnostic Dynamics Priors for Deep Reinforcement Learning

• Policy Consolidation for Continual Reinforcement Learning

探索-利用及模型参数

• Exploration Conscious Reinforcement Learning Revisited

• Dynamic Weights in Multi-Objective Deep Reinforcement Learning

• Control Regularization for Reduced Variance Reinforcement Learning

• Dead-ends and Secure Exploration in Reinforcement Learning

• Off-Policy Deep Reinforcement Learning without Exploration

• Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning

• Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations

• On the Generalization Gap in Reparameterizable Reinforcement Learning

多智能体

• Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

• CURIOUS: Intrinsically Motivated Multi-Task, Multi-Goal Reinforcement Learning

• Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning

• Maximum Entropy-Regularized Multi-Goal Reinforcement Learning

• Multi-Agent Adversarial Inverse Reinforcement Learning

• Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI

• QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

• Actor-Attention-Critic for Multi-Agent Reinforcement Learning

图模型强化学习

• TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning

• SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning

分布式强化学习

• Statistics and Samples in Distributional Reinforcement Learning

• Distribution Reinforcement Learning for Efficient Exploration

应用类

• Action Robust Reinforcement Learning and Applications in Continuous Control

• Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation

• Learning Action Representations for Reinforcement Learning

• The Value Function Polytope in Reinforcement Learning

• Generative Adversarial User Model for Reinforcement Learning Based Recommendation System

其他

• Kernel-Based Reinforcement Learning in Robust Markov Decision Processes

• A Deep Reinforcement Learning Perspective on Internet Congestion Control

• Reinforcement Learning in Configurable Continuous Environments

• Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds

注：部分文章还没有在arxiv上，或者没有的请自行Google

来源：icml2019 conference

编辑：深度强化学习算法（ID：Deep-RL）

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