ICML 2019 深度強化學習文章匯總

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深度強化學習-Report

來源：icml2019 conference

強化學習是一種通用的學習、預測和決策範式。RL為順序決策問題提供了解決方法，並將其轉化為順序決策問題。RL與優化、統計學、博弈論、因果推理、序貫實驗等有著深刻的聯繫，與近似動態規劃和最優控制有著很大的重疊，在科學、工程和藝術領域有著廣泛的應用。

RL最近在學術界取得了穩定的進展，如Atari遊戲、AlphaGo、VisuoMotor機器人政策。RL也被應用於現實場景，如推薦系統和神經架構搜索。請參閱有關RL應用程序的最新集合。希望RL系統能夠在現實世界中工作，並具有實際的好處。然而，RL存在著許多問題，如泛化、樣本效率、勘探與開發困境等。因此，RL遠未被廣泛部署。對於RL社區來說，常見的、關鍵的和緊迫的問題是：RL是否有廣泛的部署？問題是什麼？如何解決這些問題？

方法類文章

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

優化類文章

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-利用及模型參數

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

多智能體

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

圖模型強化學習

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

SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning

分散式強化學習

Statistics and Samples in Distributional Reinforcement Learning

Distribution Reinforcement Learning for Efficient Exploration

應用類

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

註：部分文章還沒有在arxiv上，或者沒有的需要自行Google

paper-PDF版本（資料獲取）

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