2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2021★ 2 cited
Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning
Trevor McInroe, Lukas Schäfer, Stefano V. Albrecht
Deep reinforcement learning (RL) agents that exist in high-dimensional state spaces, such as those composed of images, have interconnected learning burdens. Agents must learn an ac…
cs.LG2021
Analyzing the Hidden Activations of Deep Policy Networks: Why Representation Matters
Trevor A. McInroe, Michael Spurrier, Jennifer Sieber +1
We analyze the hidden activations of neural network policies of deep reinforcement learning (RL) agents and show, empirically, that it's possible to know a priori if a state repres…
cs.LG2020
Sample Efficiency in Sparse Reinforcement Learning: Or Your Money Back
Trevor A. McInroe
Sparse rewards present a difficult problem in reinforcement learning and may be inevitable in certain domains with complex dynamics such as real-world robotics. Hindsight Experienc…