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SimGAN: Hybrid Simulator Identification for Domain Adaptation via Adversarial Reinforcement Learning
As learning-based approaches progress towards automating robot controlle...
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Evolving Reinforcement Learning Algorithms
We propose a method for meta-learning reinforcement learning algorithms ...
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Model-Based Visual Planning with Self-Supervised Functional Distances
A generalist robot must be able to complete a variety of tasks in its en...
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ViNG: Learning Open-World Navigation with Visual Goals
We propose a learning-based navigation system for reaching visually indi...
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Variable-Shot Adaptation for Online Meta-Learning
Few-shot meta-learning methods consider the problem of learning new task...
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WILDS: A Benchmark of in-the-Wild Distribution Shifts
Distribution shifts can cause significant degradation in a broad range o...
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Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) methods have shown strong samp...
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Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
A wide range of reinforcement learning (RL) problems - including robustn...
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Parrot: Data-Driven Behavioral Priors for Reinforcement Learning
Reinforcement learning provides a general framework for flexible decisio...
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C-Learning: Learning to Achieve Goals via Recursive Classification
We study the problem of predicting and controlling the future state dist...
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Reinforcement Learning with Videos: Combining Offline Observations with Interaction
Reinforcement learning is a powerful framework for robots to acquire ski...
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Continual Learning of Control Primitives: Skill Discovery via Reset-Games
Reinforcement learning has the potential to automate the acquisition of ...
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Amortized Conditional Normalized Maximum Likelihood
While deep neural networks provide good performance for a range of chall...
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Rearrangement: A Challenge for Embodied AI
We describe a framework for research and evaluation in Embodied AI. Our ...
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COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning
Reinforcement learning has been applied to a wide variety of robotics pr...
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Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning
We identify an implicit under-parameterization phenomenon in value-based...
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Conservative Safety Critics for Exploration
Safe exploration presents a major challenge in reinforcement learning (R...
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γ-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction
We introduce the γ-model, a predictive model of environment dynamics wit...
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One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL
While reinforcement learning algorithms can learn effective policies for...
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MELD: Meta-Reinforcement Learning from Images via Latent State Models
Meta-reinforcement learning algorithms can enable autonomous agents, suc...
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OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning
Reinforcement learning (RL) has achieved impressive performance in a var...
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LaND: Learning to Navigate from Disengagements
Consistently testing autonomous mobile robots in real world scenarios is...
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Learning Social Learning
Social learning is a key component of human and animal intelligence. By ...
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Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings
Reinforcement learning (RL) in real-world safety-critical target setting...
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Offline Meta-Reinforcement Learning with Advantage Weighting
Massive datasets have proven critical to successfully applying deep lear...
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Assisted Perception: Optimizing Observations to Communicate State
We aim to help users estimate the state of the world in tasks like robot...
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Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift
A fundamental assumption of most machine learning algorithms is that the...
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Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions
This paper seeks to establish a framework for directing a society of sim...
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Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems
Modeling a structured, dynamic environment like a video game requires ke...
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Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?
Out-of-training-distribution (OOD) scenarios are a common challenge of l...
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Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers
We propose a simple, practical, and intuitive approach for domain adapta...
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Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors
The ability to predict and plan into the future is fundamental for agent...
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Simple and Effective VAE Training with Calibrated Decoders
Variational autoencoders (VAEs) provide an effective and simple method f...
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Ecological Reinforcement Learning
Much of the current work on reinforcement learning studies episodic sett...
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Learning Invariant Representations for Reinforcement Learning without Reconstruction
We study how representation learning can accelerate reinforcement learni...
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Accelerating Online Reinforcement Learning with Offline Datasets
Reinforcement learning provides an appealing formalism for learning cont...
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RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real
Deep neural network based reinforcement learning (RL) can learn appropri...
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Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling
Reinforcement learning algorithms can acquire policies for complex tasks...
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Conservative Q-Learning for Offline Reinforcement Learning
Effectively leveraging large, previously collected datasets in reinforce...
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MOPO: Model-based Offline Policy Optimization
Offline reinforcement learning (RL) refers to the problem of learning po...
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Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
In this tutorial article, we aim to provide the reader with the conceptu...
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Meta-Reinforcement Learning for Robotic Industrial Insertion Tasks
Robotic insertion tasks are characterized by contact and friction mechan...
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Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning
Reinforcement learning provides a general framework for learning robotic...
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The Ingredients of Real-World Robotic Reinforcement Learning
The success of reinforcement learning for real world robotics has been, ...
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The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget
In many applications, it is desirable to extract only the relevant infor...
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Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads
Transporting suspended payloads is challenging for autonomous aerial veh...
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Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation
One of the great promises of robot learning systems is that they will be...
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D4RL: Datasets for Deep Data-Driven Reinforcement Learning
The offline reinforcement learning (RL) problem, also referred to as bat...
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Datasets for Data-Driven Reinforcement Learning
The offline reinforcement learning (RL) problem, also referred to as bat...
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Thinking While Moving: Deep Reinforcement Learning with Concurrent Control
We study reinforcement learning in settings where sampling an action fro...
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