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The 2021 schedule is still incomplete
Fri Jul 23 06:00 AM -- 10:00 PM (PDT)
Reinforcement Learning for Real Life
Yuxi Li · Minmin Chen · Omer Gottesman · Lihong Li · Zongqing Lu · Rupam Mahmood · Niranjani Prasad · Zhiwei (Tony) Qin · Csaba Szepesvari · Matthew Taylor

Reinforcement learning (RL) is a general learning, predicting, and decision making paradigm and applies broadly in many disciplines, including science, engineering and humanities. RL has seen prominent successes in many problems, such as games, robotics, recommender systems. However, applying RL in the real world remains challenging, and a natural question is:

Why isn’t RL used even more often and how can we improve this?

The main goals of the workshop are to: (1) identify key research problems that are critical for the success of real-world applications; (2) report progress on addressing these critical issues; and (3) have practitioners share their success stories of applying RL to real-world problems, and the insights gained from such applications.

We invite paper submissions successfully applying RL algorithms to real-life problems and/or addressing practically relevant RL issues. Our topics of interest are general, including (but not limited to): 1) practical RL algorithms, which covers all algorithmic challenges of RL, especially those that directly address challenges faced by real-world applications; 2) practical issues: generalization, sample efficiency, exploration, reward, scalability, model-based learning, prior knowledge, safety, accountability, interpretability, reproducibility, hyper-parameter tuning; and 3) applications.

We have 6 premier panel discussions and 70+ great papers/posters. Welcome!

Poster Session
RL Foundation Panel (Panel Discussion)
RL Explainability & Interpretability Panel (Panel Discussion)
RL + Robotics Panel (Panel Discussion)
RL + Recommender Systems Panel (Panel Discussion)
RL Research-to-RealLife Gap Panel (Panel Discussion)
RL + Operations Research Panel (Panel Discussion)
Poster Session
Workshop ends (Break)
Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention (Poster)
Value-Based Deep Reinforcement Learning Requires Explicit Regularization (Poster)
Automating Power Networks: Improving RL Agent Robustness with Adversarial Training (Poster)
Understanding the Generalization Gap in Visual Reinforcement Learning (Poster)
Constraints Penalized Q-Learning for Safe Offline Reinforcement Learning (Poster)
Designing Interpretable Approximations to Deep Reinforcement Learning (Poster)
ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control (Poster)
Reinforcement Learning with Logical Action-Aware Features for Polymer Discovery (Poster)
Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces (Poster)
Off-Policy Evaluation with General Logging Policies (Poster)
Objective Robustness in Deep Reinforcement Learning (Poster)
Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage (Poster)
Avoiding Overfitting to the Importance Weights in Offline Policy Optimization (Poster)
Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer (Poster)
The Reflective Explorer: Online Meta-Exploration from Offline Data in Visual Tasks with Sparse Rewards (Poster)
What Can I Do Here? Learning New Skills by Imagining Visual Affordances (Poster)
Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks (Poster)
IV-RL: Leveraging Target Uncertainty Estimation for Sample Efficiency in Deep Reinforcement Learning (Poster)
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings (Poster)
De novo drug design using reinforcement learning with graph-based deep generative models (Poster)
Designing Online Advertisements via Bandit and Reinforcement Learning (Poster)
Efficient Exploration by HyperDQN in Deep Reinforcement Learning (Poster)
Symbolic Relational Deep Reinforcement Learning based on Graph Neural Networks (Poster)
Offline Reinforcement Learning as Anti-Exploration (Poster)
Hierarchical Multiple-Instance Data Classification with Costly Features (Poster)
Reinforcement Learning as One Big Sequence Modeling Problem (Poster)
Representation Learning for Out-of-distribution Generalization in Downstream Tasks (Poster)
Evaluating the progress of Deep Reinforcement Learning in the real world: aligning domain-agnostic and domain-specific research (Poster)
Learning Vision-Guided Quadrupedal Locomotionwith Cross-Modal Transformers (Poster)
Coordinate-wise Control Variates for Deep Policy Gradients (Poster)
Reward-Free Attacks in Multi-Agent Reinforcement Learning (Poster)
OffWorld Gym: Open-Access Physical Robotics Environment for Real-World Reinforcement Learning Benchmark and Research (Poster)
Contingency-Aware Influence Maximization: A Reinforcement Learning Approach (Poster)
Continuous Doubly Constrained Batch Reinforcement Learning (Poster)
Data-Pooling Reinforcement Learning for Personalized Healthcare Intervention (Poster)
Is Bang-Bang Control All You Need? (Poster)
The MineRL Competitions at NeurIPS 2021 (Poster)
Continual Meta Policy Search for Sequential Multi-Task Learning (Poster)
Deep Reinforcement Learning for 3D Furniture Layout in Indoor Graphic Scenes (Poster)
Robust Risk-Sensitive Reinforcement Learning Agents for Trading Markets (Poster)
Improving Human Decision-Making with Machine Learning (Poster)
AppBuddy: Learning to Accomplish Tasks in Mobile Apps via Reinforcement Learning (Poster)
Topological Experience Replay for Fast Q-Learning (Poster)
Semantic Tracklets: An Object-Centric Representation for Visual Multi-Agent Reinforcement Learning (Poster)
Multi-agent Deep Covering Option Discovery (Poster)
Learning Space Partitions for Path Planning (Poster)
Learning to Represent State with Perceptual Schemata (Poster)
Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations (Poster)
On the Difficulty of Generalizing Reinforcement Learning Framework for Combinatorial Optimization (Poster)
DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning (Poster)
Neural Rate Control for Video Encoding using Imitation Learning (Poster)
Corruption Robust Offline Reinforcement Learning (Poster)
Mind the Gap: Safely Bridging Offline and Online Reinforcement Learning (Poster)
Optimizing Dynamic Treatment Regimes via Volatile Contextual Gaussian Process Bandits (Poster)
Multi-Task Offline Reinforcement Learning with Conservative Data Sharing (Poster)
Reinforcement Learning for (Mixed) Integer Programming: Smart Feasibility Pump (Poster)
ReLMM: Practical RL for Learning Mobile Manipulation Skills Using Only Onboard Sensors (Poster)
Disentangled Attention as Intrinsic Regularization for Bimanual Multi-Object Manipulation (Poster)
RRL: Resnet as representation for Reinforcement Learning (Poster)
Decision Transformer: Reinforcement Learning via Sequence Modeling (Poster)
MobILE: Model-Based Imitation Learning From Observation Alone (Poster)
Deploying a Machine Learning System for COVID-19 Testing in Greece (Poster)
Learning a Markov Model for evaluating Soccer Decision Making (Poster)
Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap (Poster)
Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention (Poster)
Optimization of high precision manufacturing by Monte Carlo Tree Search (Poster)
Reward Shaping for User Satisfaction in a REINFORCE Recommender (Poster)
Revisiting Design Choices in Offline Model Based Reinforcement Learning (Poster)
IGLU: Interactive Grounded Language Understanding in a Collaborative Environment (Poster)
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems (Poster)
Automatic Risk Adaptation in Distributional Reinforcement Learning (Poster)
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model (Poster)
Reinforcement Learning Agent Training with Goals for Real World Tasks (Poster)
Hierarchical Imitation Learning with Contextual Bandits for DynamicTreatment Regimes (Poster)
A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning (Poster)