no code implementations • NeurIPS 2021 • Kate Rakelly, Abhishek Gupta, Carlos Florensa, Sergey Levine
Mutual information maximization provides an appealing formalism for learning representations of data.
no code implementations • 21 May 2020 • Michelle A. Lee, Carlos Florensa, Jonathan Tremblay, Nathan Ratliff, Animesh Garg, Fabio Ramos, Dieter Fox
Traditional robotic approaches rely on an accurate model of the environment, a detailed description of how to perform the task, and a robust perception system to keep track of the current state.
1 code implementation • NeurIPS 2019 • Yiming Ding, Carlos Florensa, Mariano Phielipp, Pieter Abbeel
Designing rewards for Reinforcement Learning (RL) is challenging because it needs to convey the desired task, be efficient to optimize, and be easy to compute.
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.
no code implementations • 15 Mar 2019 • Xingyu Lin, Pengsheng Guo, Carlos Florensa, David Held
Robots that are trained to perform a task in a fixed environment often fail when facing unexpected changes to the environment due to a lack of exploration.
1 code implementation • 3 Jan 2019 • Carlos Florensa, Jonas Degrave, Nicolas Heess, Jost Tobias Springenberg, Martin Riedmiller
Operating directly from raw high dimensional sensory inputs like images is still a challenge for robotic control.
no code implementations • 17 Jul 2017 • Carlos Florensa, David Held, Markus Wulfmeier, Michael Zhang, Pieter Abbeel
The robot is trained in reverse, gradually learning to reach the goal from a set of start states increasingly far from the goal.
1 code implementation • ICML 2018 • Carlos Florensa, David Held, Xinyang Geng, Pieter Abbeel
Instead, we propose a method that allows an agent to automatically discover the range of tasks that it is capable of performing.
2 code implementations • 10 Apr 2017 • Carlos Florensa, Yan Duan, Pieter Abbeel
Then a high-level policy is trained on top of these skills, providing a significant improvement of the exploration and allowing to tackle sparse rewards in the downstream tasks.
Hierarchical Reinforcement Learning reinforcement-learning +1