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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 learning…
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End-to-End Training of Deep Visuomotor Policies
TLDR
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.
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Unsupervised Learning for Physical Interaction through Video Prediction
TLDR
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.
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Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
TLDR
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.
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Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
TLDR
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.
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Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
TLDR
An open-source simulated benchmark for meta-reinforcement learning and multi-task learning consisting of 50 distinct robotic manipulation tasks is proposed to make it possible to develop algorithms that generalize to accelerate the acquisition of entirely new, held-out tasks.
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Gradient Surgery for Multi-Task Learning
TLDR
This work identifies a set of three conditions of the multi-task optimization landscape that cause detrimental gradient interference, and develops a simple yet general approach for avoiding such interference between task gradients.
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MOPO: Model-based Offline Policy Optimization
TLDR
A new model-based offline RL algorithm is proposed that applies the variance of a Lipschitz-regularized model as a penalty to the reward function, and it is found that this algorithm outperforms both standard model- based RL methods and existing state-of-the-art model-free offline RL approaches on existing offline RL benchmarks, as well as two challenging continuous control tasks.
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Stochastic Variational Video Prediction
TLDR
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.
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Meta-Learning with Implicit Gradients
TLDR
Theoretically, it is proved that implicit MAML can compute accurate meta-gradients with a memory footprint that is, up to small constant factors, no more than that which is required to compute a single inner loop gradient and at no overall increase in the total computational cost.
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