172 citations · 312 across the 7 of their papers we have counts for
4 papers · 1 filter
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL
Saurabh Kumar, Aviral Kumar, Sergey Levine +1
While reinforcement learning algorithms can learn effective policies for complex tasks, these policies are often brittle to even minor task variations, especially when variations a…
Gradient Surgery for Multi-Task Learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta +3
While deep learning and deep reinforcement learning (RL) systems have demonstrated impressive results in domains such as image classification, game playing, and robotic control, da…
DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Carles Gelada, Saurabh Kumar, Jacob Buckman +2
Many reinforcement learning (RL) tasks provide the agent with high-dimensional observations that can be simplified into low-dimensional continuous states. To formalize this process…
Dopamine: A Research Framework for Deep Reinforcement Learning
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada +2
Deep reinforcement learning (deep RL) research has grown significantly in recent years. A number of software offerings now exist that provide stable, comprehensive implementations…