1 citations · 1 across the 1 of their papers we have counts for
3 papers
Better Safe than Sorry: Evidence Accumulation Allows for Safe Reinforcement Learning
Akshat Agarwal, Abhinau Kumar, Kyle Dunovan +3
In the real world, agents often have to operate in situations with incomplete information, limited sensing capabilities, and inherently stochastic environments, making individual o…
Combining imagination and heuristics to learn strategies that generalize
Erik J Peterson, Necati Alp Müyesser, Timothy Verstynen +1
Deep reinforcement learning can match or exceed human performance in stable contexts, but with minor changes to the environment artificial networks, unlike humans, often cannot ada…
Learning model-based strategies in simple environments with hierarchical q-networks
Necati Alp Muyesser, Kyle Dunovan, Timothy Verstynen
Recent advances in deep learning have allowed artificial agents to rival human-level performance on a wide range of complex tasks; however, the ability of these networks to learn g…