no code implementations • 1 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.
1 code implementation • NeurIPS 2020 • Younggyo Seo, Kimin Lee, Ignasi Clavera, Thanard Kurutach, Jinwoo Shin, Pieter Abbeel
Model-based reinforcement learning (RL) has shown great potential in various control tasks in terms of both sample-efficiency and final performance.
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.
no code implementations • 16 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.
1 code implementation • 28 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 reinforcement-learning +1
2 code implementations • 3 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.
no code implementations • ICLR 2020 • Alexander C. Li, Carlos Florensa, Ignasi Clavera, Pieter Abbeel
Hierarchical reinforcement learning is a promising approach to tackle long-horizon decision-making problems with sparse rewards.
6 code implementations • ICLR 2019 • Jonas Rothfuss, Dennis Lee, Ignasi Clavera, Tamim Asfour, Pieter Abbeel
Credit assignment in Meta-reinforcement learning (Meta-RL) is still poorly understood.
1 code implementation • 14 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 reinforcement-learning +1
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.
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.