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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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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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f-IRL: Inverse Reinforcement Learning via State Marginal Matching
Imitation learning is well-suited for robotic tasks where it is difficul...
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Learning to be Safe: Deep RL with a Safety Critic
Safety is an essential component for deploying reinforcement learning (R...
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Interactive Visualization for Debugging RL
Visualization tools for supervised learning allow users to interpret, in...
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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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Weakly-Supervised Reinforcement Learning for Controllable Behavior
Reinforcement learning (RL) is a powerful framework for learning to take...
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Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement
Multi-task reinforcement learning (RL) aims to simultaneously learn poli...
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Learning To Reach Goals Without Reinforcement Learning
Imitation learning algorithms provide a simple and straightforward appro...
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If MaxEnt RL is the Answer, What is the Question?
Experimentally, it has been observed that humans and animals often make ...
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Efficient Exploration via State Marginal Matching
To solve tasks with sparse rewards, reinforcement learning algorithms mu...
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Search on the Replay Buffer: Bridging Planning and Reinforcement Learning
The history of learning for control has been an exciting back and forth ...
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Unsupervised Meta-Learning for Reinforcement Learning
Meta-learning is a powerful tool that builds on multi-task learning to l...
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Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
In this work, we take a representation learning perspective on hierarchi...
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Diversity is All You Need: Learning Skills without a Reward Function
Intelligent creatures can explore their environments and learn useful sk...
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Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning
Deep reinforcement learning algorithms can learn complex behavioral skil...
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Who is Mistaken?
Recognizing when people have false beliefs is crucial for understanding ...
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