382 citations · 550 across the 10 of their papers we have counts for
4 papers · 1 filter
Meta-Model-Based Meta-Policy Optimization
Takuya Hiraoka, Takahisa Imagawa, Voot Tangkaratt +3
Model-based meta-reinforcement learning (RL) methods have recently been shown to be a promising approach to improving the sample efficiency of RL in multi-task settings. However, t…
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…
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…
Hierarchical Policy Search via Return-Weighted Density Estimation
Takayuki Osa, Masashi Sugiyama
Learning an optimal policy from a multi-modal reward function is a challenging problem in reinforcement learning (RL). Hierarchical RL (HRL) tackles this problem by learning a hier…