Search Results for author: Carlos Florensa

Found 9 papers, 4 papers with code

Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning

no code implementations21 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.

Goal-conditioned Imitation Learning

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.

Imitation Learning

Adaptive Variance for Changing Sparse-Reward Environments

no code implementations15 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.

Reverse Curriculum Generation for Reinforcement Learning

no code implementations17 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.

Automatic Goal Generation for Reinforcement Learning Agents

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.

Stochastic Neural Networks for Hierarchical Reinforcement Learning

1 code implementation10 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

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