3 papers
cs.RO2023
Differentially Encoded Observation Spaces for Perceptive Reinforcement Learning
Lev Grossman, Brian Plancher
Perceptive deep reinforcement learning (DRL) has lead to many recent breakthroughs for complex AI systems leveraging image-based input data. Applications of these results range fro…
cs.RO2022
Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic Locomotion
Lev Grossman, Brian Plancher
Deep reinforcement learning (DRL) is one of the most powerful tools for synthesizing complex robotic behaviors. But training DRL models is incredibly compute and memory intensive,…
cs.RO2019
A Comparison of Action Spaces for Learning Manipulation Tasks
Patrick Varin, Lev Grossman, Scott Kuindersma
Designing reinforcement learning (RL) problems that can produce delicate and precise manipulation policies requires careful choice of the reward function, state, and action spaces.…