4 citations · 4 across the 1 of their papers we have counts for
3 papers
cs.RO2023★ 4 cited
Simulate Less, Expect More: Bringing Robot Swarms to Life via Low-Fidelity Simulations
Ricardo Vega, Kevin Zhu, Sean Luke +2
This paper proposes a novel methodology for addressing the simulation-reality gap for multi-robot swarm systems. Rather than immediately try to shrink or `bridge the gap' anytime a…
cs.LG2018
Hierarchical Approaches for Reinforcement Learning in Parameterized Action Space
Ermo Wei, Drew Wicke, Sean Luke
We explore Deep Reinforcement Learning in a parameterized action space. Specifically, we investigate how to achieve sample-efficient end-to-end training in these tasks. We propose…
cs.AI2018
Multiagent Soft Q-Learning
Ermo Wei, Drew Wicke, David Freelan +1
Policy gradient methods are often applied to reinforcement learning in continuous multiagent games. These methods perform local search in the joint-action space, and as we show, th…