382 citations · 550 across the 10 of their papers we have counts for
3 papers · 1 filter
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics
Johannes Ackermann, Volker Gabler, Takayuki Osa +1
Many real world tasks require multiple agents to work together. Multi-agent reinforcement learning (RL) methods have been proposed in recent years to solve these tasks, but current…
Goal-Conditioned Variational Autoencoder Trajectory Primitives with Continuous and Discrete Latent Codes
Takayuki Osa, Shuhei Ikemoto
Imitation learning is an intuitive approach for teaching motion to robotic systems. Although previous studies have proposed various methods to model demonstrated movement primitive…
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization
Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
Real-world tasks are often highly structured. Hierarchical reinforcement learning (HRL) has attracted research interest as an approach for leveraging the hierarchical structure of…