39 citations · 94 across the 10 of their papers we have counts for
5 papers · 1 filter
On the Role of Weight Sharing During Deep Option Learning
Matthew Riemer, Ignacio Cases, Clemens Rosenbaum +2
The options framework is a popular approach for building temporally extended actions in reinforcement learning. In particular, the option-critic architecture provides general purpo…
Hierarchical Average Reward Policy Gradient Algorithms
Akshay Dharmavaram, Matthew Riemer, Shalabh Bhatnagar
Option-critic learning is a general-purpose reinforcement learning (RL) framework that aims to address the issue of long term credit assignment by leveraging temporal abstractions.…
Routing Networks and the Challenges of Modular and Compositional Computation
Clemens Rosenbaum, Ignacio Cases, Matthew Riemer +1
Compositionality is a key strategy for addressing combinatorial complexity and the curse of dimensionality. Recent work has shown that compositional solutions can be learned and of…
Continual Learning with Self-Organizing Maps
Pouya Bashivan, Martin Schrimpf, Robert Ajemian +3
Despite remarkable successes achieved by modern neural networks in a wide range of applications, these networks perform best in domain-specific stationary environments where they a…
Learning Hierarchical Teaching Policies for Cooperative Agents
Dong-Ki Kim, Miao Liu, Shayegan Omidshafiei +7
Collective learning can be greatly enhanced when agents effectively exchange knowledge with their peers. In particular, recent work studying agents that learn to teach other teamma…