4 citations · 6 across the 4 of their papers we have counts for
4 papers
Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning
Ali Rahimi-Kalahroudi, Janarthanan Rajendran, Ida Momennejad +2
One of the key behavioral characteristics used in neuroscience to determine whether the subject of study -- be it a rodent or a human -- exhibits model-based learning is effective…
Neuro-Nav: A Library for Neurally-Plausible Reinforcement Learning
Arthur Juliani, Samuel Barnett, Brandon Davis +2
In this work we propose Neuro-Nav, an open-source library for neurally plausible reinforcement learning (RL). RL is among the most common modeling frameworks for studying decision…
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
Yi Wan, Ali Rahimi-Kalahroudi, Janarthanan Rajendran +3
In recent years, a growing number of deep model-based reinforcement learning (RL) methods have been introduced. The interest in deep model-based RL is not surprising, given its man…
One-Shot Learning from a Demonstration with Hierarchical Latent Language
Nathaniel Weir, Xingdi Yuan, Marc-Alexandre Côté +5
Humans have the capability, aided by the expressive compositionality of their language, to learn quickly by demonstration. They are able to describe unseen task-performing procedur…