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Found 25 papers, 11 papers with code
Latent-Variable Advantage-Weighted Policy Optimization for Offline RL
Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions.
How to Leverage Unlabeled Data in Offline Reinforcement Learning
One natural solution is to learn a reward function from the labeled data and use it to label the unlabeled data.
Data Sharing without Rewards in Multi-Task Offline Reinforcement Learning
However, these benefits come at a cost -- for data to be shared between tasks, each transition must be annotated with reward labels corresponding to other tasks.
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning
We argue that a natural use case of offline RL is in settings where we can pool large amounts of data collected in various scenarios for solving different tasks, and utilize all of this data to learn behaviors for all the tasks more effectively rather than training each one in isolation.
Efficiently Identifying Task Groupings for Multi-Task Learning
Multi-task learning can leverage information learned by one task to benefit the training of other tasks.
Visual Adversarial Imitation Learning using Variational Models
We consider a setting where an agent is provided a fixed dataset of visual demonstrations illustrating how to perform a task, and must learn to solve the task using the provided demonstrations and unsupervised environment interactions.
Variational Model-Based Imitation Learning in High-Dimensional Observation Spaces
We consider the problem setting of imitation learning where the agent is provided a fixed dataset of demonstrations.
COMBO: Conservative Offline Model-Based Policy Optimization
We overcome this limitation by developing a new model-based offline RL algorithm, COMBO, that regularizes the value function on out-of-support state-action tuples generated via rollouts under the learned model.
Information Transfer in Multi-Task Learning
Multi-task learning can leverage information learned by one task to benefit the training of other tasks.
Variable-Shot Adaptation for Incremental Meta-Learning
Few-shot meta-learning methods consider the problem of learning new tasks from a small, fixed number of examples, by meta-learning across static data from a set of previous tasks.
Offline Reinforcement Learning from Images with Latent Space Models
In this work, we build on recent advances in model-based algorithms for offline RL, and extend them to high-dimensional visual observation spaces.
Variable-Shot Adaptation for Online Meta-Learning
Few-shot meta-learning methods consider the problem of learning new tasks from a small, fixed number of examples, by meta-learning across static data from a set of previous tasks.
Measuring and Harnessing Transference in Multi-Task Learning
Multi-task learning can leverage information learned by one task to benefit the training of other tasks.
MOPO: Model-based Offline Policy Optimization
We also characterize the trade-off between the gain and risk of leaving the support of the batch data.
Gradient Surgery for Multi-Task Learning
While deep learning and deep reinforcement learning (RL) systems have demonstrated impressive results in domains such as image classification, game playing, and robotic control, data efficiency remains a major challenge.
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Therefore, if the aim of these methods is to enable faster acquisition of entirely new behaviors, we must evaluate them on task distributions that are sufficiently broad to enable generalization to new behaviors.
Ranked #1 on
Meta-Learning
on ML10
Mint: Matrix-Interleaving for Multi-Task Learning
Deep learning enables training of large and flexible function approximators from scratch at the cost of large amounts of data.
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
Critically, our model can infer rewards for new, structurally-similar tasks from a single demonstration.
Ranked #1 on
MuJoCo Games
on Sawyer Pusher
Learning to Reinforcement Learn by Imitation
Meta-reinforcement learning aims to learn fast reinforcement learning (RL) procedures that can be applied to new tasks or environments.
Unsupervised Visuomotor Control through Distributional Planning Networks
While reinforcement learning (RL) has the potential to enable robots to autonomously acquire a wide range of skills, in practice, RL usually requires manual, per-task engineering of reward functions, especially in real world settings where aspects of the environment needed to compute progress are not directly accessible.
One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks
We consider the problem of learning multi-stage vision-based tasks on a real robot from a single video of a human performing the task, while leveraging demonstration data of subtasks with other objects.
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning
Humans and animals are capable of learning a new behavior by observing others perform the skill just once.
One-Shot Visual Imitation Learning via Meta-Learning
In this work, we present 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.
Real-Time User-Guided Image Colorization with Learned Deep Priors
The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a Convolutional Neural Network (CNN).
Generalizing Skills with Semi-Supervised Reinforcement Learning
We evaluate our method on challenging tasks that require control directly from images, and show that our approach can improve the generalization of a learned deep neural network policy by using experience for which no reward function is available.
