184 citations · 542 across the 9 of their papers we have counts for
9 papers
Which Mutual-Information Representation Learning Objectives are Sufficient for Control?
Kate Rakelly, Abhishek Gupta, Carlos Florensa +1
Mutual information maximization provides an appealing formalism for learning representations of data. In the context of reinforcement learning (RL), such representations can accele…
Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning
Michelle A. Lee, Carlos Florensa, Jonathan Tremblay +4
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…
Sub-policy Adaptation for Hierarchical Reinforcement Learning
Alexander C. Li, Carlos Florensa, Ignasi Clavera +1
Hierarchical reinforcement learning is a promising approach to tackle long-horizon decision-making problems with sparse rewards. Unfortunately, most methods still decouple the lowe…
Goal-conditioned Imitation Learning
Yiming Ding, Carlos Florensa, Mariano Phielipp +1
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. The latter is partic…
Adaptive Variance for Changing Sparse-Reward Environments
Xingyu Lin, Pengsheng Guo, Carlos Florensa +1
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. We propose a principled…
Self-supervised Learning of Image Embedding for Continuous Control
Carlos Florensa, Jonas Degrave, Nicolas Heess +2
Operating directly from raw high dimensional sensory inputs like images is still a challenge for robotic control. Recently, Reinforcement Learning methods have been proposed to sol…
Reverse Curriculum Generation for Reinforcement Learning
Carlos Florensa, David Held, Markus Wulfmeier +2
Many relevant tasks require an agent to reach a certain state, or to manipulate objects into a desired configuration. For example, we might want a robot to align and assemble a gea…
Automatic Goal Generation for Reinforcement Learning Agents
Carlos Florensa, David Held, Xinyang Geng +1
Reinforcement learning is a powerful technique to train an agent to perform a task. However, an agent that is trained using reinforcement learning is only capable of achieving the…
Stochastic Neural Networks for Hierarchical Reinforcement Learning
Carlos Florensa, Yan Duan, Pieter Abbeel
Deep reinforcement learning has achieved many impressive results in recent years. However, tasks with sparse rewards or long horizons continue to pose significant challenges. To ta…