| 1 |
Robust Adversarial Reinforcement Learning |
| 2 |
Mastering the game of Go with deep neural networks and tree search |
| 3 |
Mastering the game of Go without human knowledge |
| 4 |
Continuous Control With Deep Reinforcement Learning |
| 5 |
Benchmarking deep reinforcement learning for continuous control |
| 6 |
Deep Reinforcement Learning for Mention-Ranking Coreference Models |
| 7 |
Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning |
| 8 |
Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access |
| 9 |
Deep Reinforcement Learning for Dialogue Generation |
| 10 |
Online Reinforcement Learning in Stochastic Games |
| 11 |
Self-critical Sequence Training for Image Captioning |
| 12 |
Improved Image Captioning via Policy Gradient optimization of SPIDEr |
| 13 |
Safe and Nested Subgame Solving for Imperfect-Information Games |
| 14 |
Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning |
| 15 |
Neural Adaptive Video Streaming with Pensieve |
| 16 |
ReasoNet: Learning to Stop Reading in Machine Comprehension |
| 17 |
Dual learning for machine translation |
| 18 |
Reinforcement Mechanism Design |
| 19 |
Tuning Recurrent Neural Networks with Reinforcement Learning |
| 20 |
Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning |
| 21 |
Designing neural network architectures using reinforcement learning |
| 22 |
Neural Architecture Search with Reinforcement Learning |
| 23 |
Task-Oriented Query Reformulation with Reinforcement Learning |
| 24 |
Ask the Right Questions: Active Question Reformulation with Reinforcement Learning |
| 25 |
Go for a Walk and Arrive at the Answer: Reasoning over Paths in Knowledge Bases using Reinforcement Learning |
| 26 |
Real-Time Bidding by Reinforcement Learning in Display Advertising |
| 27 |
Dynamic Scholarly Collaborator Recommendation via Competitive Multi-Agent Reinforcement Learning |
| 28 |
DRN: A Deep Reinforcement Learning Framework for News Recommendation |
| 29 |
Reinforcement Learning for Relation Classification from Noisy Data |
| 30 |
Resource Management with Deep Reinforcement Learning |
| 31 |
Model-Based Reinforcement Learning in Continuous Environments using real-time constrained optimization |
| 32 |
End-to-End Training of Deep Visuomotor Policies |
| 33 |
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates |
| 34 |
Learning To Route |
| 35 |
Learning Structured Representation for Text Classification via Reinforcement Learning |
| 36 |
Generating Text with Deep Reinforcement Learning |
| 37 |
A Deep Reinforced Model for Abstractive Summarization |
| 38 |
Experience-driven Networking A Deep Reinforcement Learning based Approach |
| 39 |
Coordinated deep reinforcement learners for traffic light control |
| 40 |
Playing FPS Games with Deep Reinforcement Learning |
| 41 |
A Deep Hierarchical Approach to Lifelong Learning in Minecraft |
| 42 |
Playing Atari with Deep Reinforcement Learning |
| 43 |
Learning to act by predicting the future |
| 44 |
Active Neural Localization |
| 45 |
Asynchronous methods for deep reinforcement learning |
| 46 |
Deep Attention Recurrent Q-Network |
| 47 |
Reinforcement Learning Neural Turing Machines - Revised |
| 48 |
Learning Deep Neural Network Policies with Continuous Memory States |
| 49 |
Dueling Network Architectures for Deep Reinforcement Learning |
| 50 |
Evolution Strategies as a Scalable Alternative to Reinforcement Learning |
| 51 |
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning |
| 52 |
Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models |
| 53 |
Curiosity-driven Exploration by Self-supervised Prediction |
| 54 |
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning |
| 55 |
Deep Exploration via Bootstrapped DQN |
| 56 |
The option-critic architecture |
| 57 |
FeUdal Networks for Hierarchical Reinforcement Learning |
| 58 |
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation |
| 59 |
Deep reinforcement learning in large discrete action spaces |
| 60 |
Learning To Reinforcement Learn |
| 61 |
Reinforcement Learning under Model Mismatch |
| 62 |
Continuous Deep Q-Learning with Model-based Acceleration |
| 63 |
Safe Model-based Reinforcement Learning with Stability Guarantees |
| 64 |
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning |
| 65 |
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning |
| 66 |
Learning to Communicate with Deep Multi-Agent Reinforcement Learning |
| 67 |
Learning multiagent communication with back-propagation |
| 68 |
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning |
| 69 |
Learning values across many orders of magnitude |
| 70 |
Deep Reinforcement Learning in Parameterized Action Space |
| 71 |
Reinforcement Learning with Parameterized Actions |
| 72 |
Value iteration networks |
| 73 |
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor |
| 74 |
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning |
| 75 |
Sample Efficient Actor-Critic with Experience Replay |
| 76 |
Policy Shaping:Integrating Human Feedback with Reinforcement Learning |
| 77 |
Interpolated policy gradient: Merging on-policy and off-policy gradient estimation for deep reinforcement learning |
| 78 |
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation |
| 79 |
Proximal Policy Optimization Algorithms |
| 80 |
Simple Random Search Provides a Competitive Approach to Reinforcement Learning |
| 81 |
Trust Region Policy Optimization |
| 82 |
Generative Adversarial Imitation Learning |
| 83 |
Where to Add Actions in Human-in-the-Loop Reinforcement Learning |
| 84 |
Maximum Entropy Deep Inverse Reinforcement Learning |
| 85 |
Cooperative inverse reinforcement learning |
| 86 |
Reinforcement Learning from Demonstration through Shaping |
| 87 |
Hybrid Reward Architecutre for Reinforcement Learning |
| 88 |
Deep Reinforcement Learning from Human Preferences |
| 89 |
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds |
| 90 |
Distral: Robust Multitask Reinforcement Learning |
| 91 |
Scalable Multitask Policy Gradient Reinforcement Learning |
| 92 |
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning |
| 93 |
Transfer Reinforcement Learning with Shared Dynamics |
| 94 |
Successor Features for Transfer in Reinforcement Learning |
| 95 |
Massively Parallel Methods for Deep Reinforcement Learning |
| 96 |
Deep reinforcement learning with double Q-learning |
| 97 |
Human-level control through deep reinforcement learning |
| 98 |
Multi-step Off-policy Learning Without Importance Sampling Ratios |
| 99 |
Weighted importance sampling for off-policy learning with linear function approximation |
| 100 |
Off-policy learning based on weighted importance sampling with linear computational complexity |
| 101 |
Safe and Efficient Off-policy Reinforcement Learning |
| 102 |
Universal Value Function Approximators |
| 103 |
Linear feature encoding for reinforcement learning |
| 104 |
Imagination-Augmented Agents for Deep Reinforcement Learning |
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