101 citations · 346 across the 24 of their papers we have counts for
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cs.LG2023
Mimicking Better by Matching the Approximate Action Distribution
João A. Cândido Ramos, Lionel Blondé, Naoya Takeishi +1
In this paper, we introduce MAAD, a novel, sample-efficient on-policy algorithm for Imitation Learning from Observations. MAAD utilizes a surrogate reward signal, which can be deri…
cs.AI2023★ 1 cited
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations
Keisuke Fujii, Kazushi Tsutsui, Atom Scott +3
Modeling of real-world biological multi-agents is a fundamental problem in various scientific and engineering fields. Reinforcement learning (RL) is a powerful framework to generat…