1 citations · 1 across the 3 of their papers we have counts for
3 papers
Investigating Action Encodings in Recurrent Neural Networks in Reinforcement Learning
Matthew Schlegel, Volodymyr Tkachuk, Adam White +1
Building and maintaining state to learn policies and value functions is critical for deploying reinforcement learning (RL) agents in the real world. Recurrent neural networks (RNNs…
Efficient Planning in Combinatorial Action Spaces with Applications to Cooperative Multi-Agent Reinforcement Learning
Volodymyr Tkachuk, Seyed Alireza Bakhtiari, Johannes Kirschner +3
A practical challenge in reinforcement learning are combinatorial action spaces that make planning computationally demanding. For example, in cooperative multi-agent reinforcement…
The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning
Volodymyr Tkachuk, Sriram Ganapathi Subramanian, Matthew E. Taylor
Some reinforcement learning methods suffer from high sample complexity causing them to not be practical in real-world situations. -function reuse, a transfer learning method, is…