Search Results for author: Dushyant Rao

Found 17 papers, 3 papers with code

Towards Compute-Optimal Transfer Learning

no code implementations25 Apr 2023 Massimo Caccia, Alexandre Galashov, Arthur Douillard, Amal Rannen-Triki, Dushyant Rao, Michela Paganini, Laurent Charlin, Marc'Aurelio Ranzato, Razvan Pascanu

The field of transfer learning is undergoing a significant shift with the introduction of large pretrained models which have demonstrated strong adaptability to a variety of downstream tasks.

Continual Learning Transfer Learning

Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains

no code implementations24 Feb 2023 Jingwei Zhang, Jost Tobias Springenberg, Arunkumar Byravan, Leonard Hasenclever, Abbas Abdolmaleki, Dushyant Rao, Nicolas Heess, Martin Riedmiller

We conduct a set of experiments in the RGB-stacking environment, showing that planning with the learned skills and the associated model can enable zero-shot generalization to new tasks, and can further speed up training of policies via reinforcement learning.

reinforcement-learning Reinforcement Learning (RL)

Probing Transfer in Deep Reinforcement Learning without Task Engineering

no code implementations22 Oct 2022 Andrei A. Rusu, Sebastian Flennerhag, Dushyant Rao, Razvan Pascanu, Raia Hadsell

By formally organising these modifications into several factors of variation, we are able to show that Analyses of Variance (ANOVA) are a potent tool for studying the effects of human-relevant domain changes on the learning and transfer performance of a deep reinforcement learning agent.

reinforcement-learning Reinforcement Learning (RL) +1

MO2: Model-Based Offline Options

no code implementations5 Sep 2022 Sasha Salter, Markus Wulfmeier, Dhruva Tirumala, Nicolas Heess, Martin Riedmiller, Raia Hadsell, Dushyant Rao

The ability to discover useful behaviours from past experience and transfer them to new tasks is considered a core component of natural embodied intelligence.

Continuous Control

Forgetting and Imbalance in Robot Lifelong Learning with Off-policy Data

no code implementations12 Apr 2022 Wenxuan Zhou, Steven Bohez, Jan Humplik, Abbas Abdolmaleki, Dushyant Rao, Markus Wulfmeier, Tuomas Haarnoja, Nicolas Heess

We propose the Offline Distillation Pipeline to break this trade-off by separating the training procedure into an online interaction phase and an offline distillation phase. Second, we find that training with the imbalanced off-policy data from multiple environments across the lifetime creates a significant performance drop.

Reinforcement Learning (RL)

Learning Transferable Motor Skills with Hierarchical Latent Mixture Policies

no code implementations ICLR 2022 Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever, Markus Wulfmeier, Martina Zambelli, Giulia Vezzani, Dhruva Tirumala, Yusuf Aytar, Josh Merel, Nicolas Heess, Raia Hadsell

We demonstrate in manipulation domains that the method can effectively cluster offline data into distinct, executable behaviours, while retaining the flexibility of a continuous latent variable model.

Task-agnostic Continual Learning with Hybrid Probabilistic Models

no code implementations ICML Workshop INNF 2021 Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao, Balaji Lakshminarayanan, Nir Levine, Ang Li, Huiyi Hu, Andrew Gordon Wilson, Razvan Pascanu

Learning new tasks continuously without forgetting on a constantly changing data distribution is essential for real-world problems but extremely challenging for modern deep learning.

Anomaly Detection Continual Learning +1

Attention-Privileged Reinforcement Learning

no code implementations19 Nov 2019 Sasha Salter, Dushyant Rao, Markus Wulfmeier, Raia Hadsell, Ingmar Posner

Image-based Reinforcement Learning is known to suffer from poor sample efficiency and generalisation to unseen visuals such as distractors (task-independent aspects of the observation space).

reinforcement-learning Reinforcement Learning (RL)

Continual Unsupervised Representation Learning

1 code implementation NeurIPS 2019 Dushyant Rao, Francesco Visin, Andrei A. Rusu, Yee Whye Teh, Razvan Pascanu, Raia Hadsell

Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially.

Continual Learning Representation Learning

Attention Privileged Reinforcement Learning for Domain Transfer

no code implementations25 Sep 2019 Sasha Salter, Dushyant Rao, Markus Wulfmeier, Raia Hadsell, Ingmar Posner

Applying reinforcement learning (RL) to physical systems presents notable challenges, given requirements regarding sample efficiency, safety, and physical constraints compared to simulated environments.

reinforcement-learning Reinforcement Learning (RL)

Meta-Learning with Latent Embedding Optimization

5 code implementations ICLR 2019 Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, Raia Hadsell

We show that it is possible to bypass these limitations by learning a data-dependent latent generative representation of model parameters, and performing gradient-based meta-learning in this low-dimensional latent space.

Few-Shot Learning

Incorporating Human Domain Knowledge into Large Scale Cost Function Learning

no code implementations13 Dec 2016 Markus Wulfmeier, Dushyant Rao, Ingmar Posner

Recent advances have shown the capability of Fully Convolutional Neural Networks (FCN) to model cost functions for motion planning in the context of learning driving preferences purely based on demonstration data from human drivers.

Motion Planning reinforcement-learning +1

Deep Tracking on the Move: Learning to Track the World from a Moving Vehicle using Recurrent Neural Networks

no code implementations29 Sep 2016 Julie Dequaire, Dushyant Rao, Peter Ondruska, Dominic Wang, Ingmar Posner

This paper presents an end-to-end approach for tracking static and dynamic objects for an autonomous vehicle driving through crowded urban environments.

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