19 citations · 31 across the 14 of their papers we have counts for
8 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…
Efficient Sparse-Reward Goal-Conditioned Reinforcement Learning with a High Replay Ratio and Regularization
Takuya Hiraoka
Reinforcement learning (RL) methods with a high replay ratio (RR) and regularization have gained interest due to their superior sample efficiency. However, these methods have mainl…
Unsupervised Discovery of Continuous Skills on a Sphere
Takahisa Imagawa, Takuya Hiraoka, Yoshimasa Tsuruoka
Recently, methods for learning diverse skills to generate various behaviors without external rewards have been actively studied as a form of unsupervised reinforcement learning. Ho…
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…
Optimistic Proximal Policy Optimization
Takahisa Imagawa, Takuya Hiraoka, Yoshimasa Tsuruoka
Reinforcement Learning, a machine learning framework for training an autonomous agent based on rewards, has shown outstanding results in various domains. However, it is known that…
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…