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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learningExpand
End-to-End Training of Deep Visuomotor Policies
This paper develops a method that can be used to learn policies that map raw image observations directly to torques at the robot's motors, trained using a partially observed guided policy search method, with supervision provided by a simple trajectory-centric reinforcement learning method. Expand
Unsupervised Learning for Physical Interaction through Video Prediction
An action-conditioned video prediction model is developed that explicitly models pixel motion, by predicting a distribution over pixel motion from previous frames, and is partially invariant to object appearance, enabling it to generalize to previously unseen objects. Expand
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
This work explores how inverse optimal control (IOC) can be used to learn behaviors from demonstrations, with applications to torque control of high-dimensional robotic systems and an efficient sample-based approximation for MaxEnt IOC. Expand
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
This paper develops an off-policy meta-RL algorithm that disentangles task inference and control and performs online probabilistic filtering of latent task variables to infer how to solve a new task from small amounts of experience. Expand
Stochastic Variational Video Prediction
This paper develops a stochastic variational video prediction (SV2P) method that predicts a different possible future for each sample of its latent variables, and is the first to provide effective Stochastic multi-frame prediction for real-world video. Expand
Stochastic Adversarial Video Prediction
This work shows that latent variational variable models that explicitly model underlying stochasticity and adversarially-trained models that aim to produce naturalistic images are in fact complementary and combines the two to produce predictions that look more realistic to human raters and better cover the range of possible futures. Expand
Probabilistic Model-Agnostic Meta-Learning
This paper proposes a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution that is trained via a variational lower bound, and shows how reasoning about ambiguity can also be used for downstream active learning problems. Expand
One-Shot Visual Imitation Learning via Meta-Learning
A meta-imitation learning method that enables a robot to learn how to learn more efficiently, allowing it to acquire new skills from just a single demonstration, and requires data from significantly fewer prior tasks for effective learning of new skills. Expand
Self-Supervised Visual Planning with Temporal Skip Connections
This work introduces a video prediction model that can keep track of objects through occlusion by incorporating temporal skip-connections and demonstrates that this model substantially outperforms prior work on video prediction-based control. Expand