10 citations · 34 across the 12 of their papers we have counts for
11 papers · 1 filter
Reinforcement Learning in Education: A Multi-Armed Bandit Approach
Herkulaas Combrink, Vukosi Marivate, Benjamin Rosman
Advances in reinforcement learning research have demonstrated the ways in which different agent-based models can learn how to optimally perform a task within a given environment. R…
Comparing Synthetic Tabular Data Generation Between a Probabilistic Model and a Deep Learning Model for Education Use Cases
Herkulaas MvE Combrink, Vukosi Marivate, Benjamin Rosman
The ability to generate synthetic data has a variety of use cases across different domains. In education research, there is a growing need to have access to synthetic data to test…
World Value Functions: Knowledge Representation for Multitask Reinforcement Learning
Geraud Nangue Tasse, Steven James, Benjamin Rosman
An open problem in artificial intelligence is how to learn and represent knowledge that is sufficient for a general agent that needs to solve multiple tasks in a given world. In th…
Learning Abstract and Transferable Representations for Planning
Steven James, Benjamin Rosman, George Konidaris
We are concerned with the question of how an agent can acquire its own representations from sensory data. We restrict our focus to learning representations for long-term planning,…
Learning to Follow Language Instructions with Compositional Policies
Vanya Cohen, Geraud Nangue Tasse, Nakul Gopalan +3
We propose a framework that learns to execute natural language instructions in an environment consisting of goal-reaching tasks that share components of their task descriptions. Ou…
Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization
Kale-ab Tessera, Sara Hooker, Benjamin Rosman
Training sparse networks to converge to the same performance as dense neural architectures has proven to be elusive. Recent work suggests that initialization is the key. However, w…