3 citations · 5 across the 6 of their papers we have counts for
5 papers · 1 filter
Multi-Task Reinforcement Learning with Language-Encoded Gated Policy Networks
Rushiv Arora
Multi-task reinforcement learning often relies on task metadata -- such as brief natural-language descriptions -- to guide behavior across diverse objectives. We present Lexical Po…
Hierarchical Universal Value Function Approximators
Rushiv Arora
There have been key advancements to building universal approximators for multi-goal collections of reinforcement learning value functions -- key elements in estimating long-term re…
On the Dynamics of Learning Time-Aware Behavior with Recurrent Neural Networks
Peter DelMastro, Rushiv Arora, Edward Rietman +1
Recurrent Neural Networks (RNNs) have shown great success in modeling time-dependent patterns, but there is limited research on their learned representations of latent temporal fea…
Locally Constrained Representations in Reinforcement Learning
Somjit Nath, Rushiv Arora, Samira Ebrahimi Kahou
The success of Reinforcement Learning (RL) heavily relies on the ability to learn robust representations from the observations of the environment. In most cases, the representation…
Model-Based Reinforcement Learning with SINDy
Rushiv Arora, Bruno Castro da Silva, Eliot Moss
We draw on the latest advancements in the physics community to propose a novel method for discovering the governing non-linear dynamics of physical systems in reinforcement learnin…