Search Results for author: Tianhe Yu

Found 25 papers, 11 papers with code

Latent-Variable Advantage-Weighted Policy Optimization for Offline RL

no code implementations16 Mar 2022 Xi Chen, Ali Ghadirzadeh, Tianhe Yu, Yuan Gao, Jianhao Wang, Wenzhe Li, Bin Liang, Chelsea Finn, Chongjie Zhang

Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions.

Continuous Control Offline RL +1

How to Leverage Unlabeled Data in Offline Reinforcement Learning

no code implementations3 Feb 2022 Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, Sergey Levine

One natural solution is to learn a reward function from the labeled data and use it to label the unlabeled data.

Offline RL reinforcement-learning

Data Sharing without Rewards in Multi-Task Offline Reinforcement Learning

no code implementations29 Sep 2021 Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Chelsea Finn, Sergey Levine, Karol Hausman

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.

Multi-Task Learning Offline RL +1

Conservative Data Sharing for Multi-Task Offline Reinforcement Learning

no code implementations NeurIPS 2021 Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn

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.

Offline RL reinforcement-learning

Visual Adversarial Imitation Learning using Variational Models

no code implementations NeurIPS 2021 Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn

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.

Imitation Learning Representation Learning

COMBO: Conservative Offline Model-Based Policy Optimization

2 code implementations NeurIPS 2021 Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn

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.

Offline RL

Information Transfer in Multi-Task Learning

no code implementations1 Jan 2021 Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn

Multi-task learning can leverage information learned by one task to benefit the training of other tasks.

Multi-Task Learning

Variable-Shot Adaptation for Incremental Meta-Learning

no code implementations1 Jan 2021 Tianhe Yu, Xinyang Geng, Chelsea Finn, Sergey Levine

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.

Meta-Learning Zero-Shot Learning

Offline Reinforcement Learning from Images with Latent Space Models

1 code implementation21 Dec 2020 Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn

In this work, we build on recent advances in model-based algorithms for offline RL, and extend them to high-dimensional visual observation spaces.

Offline RL reinforcement-learning

Variable-Shot Adaptation for Online Meta-Learning

no code implementations14 Dec 2020 Tianhe Yu, Xinyang Geng, Chelsea Finn, Sergey Levine

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.

Meta-Learning Zero-Shot Learning

Measuring and Harnessing Transference in Multi-Task Learning

no code implementations29 Oct 2020 Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn

Multi-task learning can leverage information learned by one task to benefit the training of other tasks.

Multi-Task Learning

Gradient Surgery for Multi-Task Learning

8 code implementations NeurIPS 2020 Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn

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.

Image Classification Multi-Task Learning

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

6 code implementations24 Oct 2019 Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Avnish Narayan, Hayden Shively, Adithya Bellathur, Karol Hausman, Chelsea Finn, Sergey Levine

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.

Meta-Learning Meta Reinforcement Learning +2

Mint: Matrix-Interleaving for Multi-Task Learning

no code implementations25 Sep 2019 Tianhe Yu, Saurabh Kumar, Eric Mitchell, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn

Deep learning enables training of large and flexible function approximators from scratch at the cost of large amounts of data.

Multi-Task Learning reinforcement-learning

Unsupervised Visuomotor Control through Distributional Planning Networks

1 code implementation14 Feb 2019 Tianhe Yu, Gleb Shevchuk, Dorsa Sadigh, Chelsea Finn

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.

reinforcement-learning

One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks

no code implementations25 Oct 2018 Tianhe Yu, Pieter Abbeel, Sergey Levine, Chelsea Finn

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.

Imitation Learning

One-Shot Visual Imitation Learning via Meta-Learning

3 code implementations14 Sep 2017 Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, Sergey Levine

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.

Imitation Learning Meta-Learning

Real-Time User-Guided Image Colorization with Learned Deep Priors

3 code implementations8 May 2017 Richard Zhang, Jun-Yan Zhu, Phillip Isola, Xinyang Geng, Angela S. Lin, Tianhe Yu, Alexei A. Efros

The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a Convolutional Neural Network (CNN).

Colorization

Generalizing Skills with Semi-Supervised Reinforcement Learning

no code implementations1 Dec 2016 Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine

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.

reinforcement-learning

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