Search Results for author: Ignasi Clavera

Found 11 papers, 5 papers with code

R-LAtte: Attention Module for Visual Control via Reinforcement Learning

no code implementations1 Jan 2021 Mandi Zhao, Qiyang Li, Aravind Srinivas, Ignasi Clavera, Kimin Lee, Pieter Abbeel

Attention mechanisms are generic inductive biases that have played a critical role in improving the state-of-the-art in supervised learning, unsupervised pre-training and generative modeling for multiple domains including vision, language and speech.

Unsupervised Pre-training

Mutual Information Maximization for Robust Plannable Representations

no code implementations16 May 2020 Yiming Ding, Ignasi Clavera, Pieter Abbeel

The later, while they present low sample complexity, they learn latent spaces that need to reconstruct every single detail of the scene.

Model-based Reinforcement Learning

Model-Augmented Actor-Critic: Backpropagating through Paths

no code implementations ICLR 2020 Ignasi Clavera, Violet Fu, Pieter Abbeel

Current model-based reinforcement learning approaches use the model simply as a learned black-box simulator to augment the data for policy optimization or value function learning.

Model-based Reinforcement Learning

Asynchronous Methods for Model-Based Reinforcement Learning

1 code implementation28 Oct 2019 Yunzhi Zhang, Ignasi Clavera, Boren Tsai, Pieter Abbeel

In this work, we propose an asynchronous framework for model-based reinforcement learning methods that brings down the run time of these algorithms to be just the data collection time.

Model-based Reinforcement Learning

Benchmarking Model-Based Reinforcement Learning

2 code implementations3 Jul 2019 Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, Jimmy Ba

Model-based reinforcement learning (MBRL) is widely seen as having the potential to be significantly more sample efficient than model-free RL.

Model-based Reinforcement Learning

Model-Based Reinforcement Learning via Meta-Policy Optimization

no code implementations14 Sep 2018 Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel

Finally, we demonstrate that our approach is able to match the asymptotic performance of model-free methods while requiring significantly less experience.

Model-based Reinforcement Learning

Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning

2 code implementations ICLR 2019 Anusha Nagabandi, Ignasi Clavera, Simin Liu, Ronald S. Fearing, Pieter Abbeel, Sergey Levine, Chelsea Finn

Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time.

Continuous Control Meta-Learning +3

Model-Ensemble Trust-Region Policy Optimization

2 code implementations ICLR 2018 Thanard Kurutach, Ignasi Clavera, Yan Duan, Aviv Tamar, Pieter Abbeel

In this paper, we analyze the behavior of vanilla model-based reinforcement learning methods when deep neural networks are used to learn both the model and the policy, and show that the learned policy tends to exploit regions where insufficient data is available for the model to be learned, causing instability in training.

Continuous Control Model-based Reinforcement Learning

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