19 citations · 44 across the 12 of their papers we have counts for
6 papers · 1 filter
Which Experiences Are Influential for RL Agents? Efficiently Estimating The Influence of Experiences
Takuya Hiraoka, Guanquan Wang, Takashi Onishi +1
In reinforcement learning (RL) with experience replay, experiences stored in a replay buffer influence the RL agent's performance. Information about how these experiences influence…
Which Experiences Are Influential for Your Agent? Policy Iteration with Turn-over Dropout
Takuya Hiraoka, Takashi Onishi, Yoshimasa Tsuruoka
In reinforcement learning (RL) with experience replay, experiences stored in a replay buffer influence the RL agent's performance. Information about the influence is valuable for v…
Dropout Q-Functions for Doubly Efficient Reinforcement Learning
Takuya Hiraoka, Takahisa Imagawa, Taisei Hashimoto +2
Randomized ensembled double Q-learning (REDQ) (Chen et al., 2021b) has recently achieved state-of-the-art sample efficiency on continuous-action reinforcement learning benchmarks.…
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…
Learning Robust Options by Conditional Value at Risk Optimization
Takuya Hiraoka, Takahisa Imagawa, Tatsuya Mori +2
Options are generally learned by using an inaccurate environment model (or simulator), which contains uncertain model parameters. While there are several methods to learn options t…
Hierarchical Reinforcement Learning with Abductive Planning
Kazeto Yamamoto, Takashi Onishi, Yoshimasa Tsuruoka
One of the key challenges in applying reinforcement learning to real-life problems is that the amount of train-and-error required to learn a good policy increases drastically as th…