59 citations · 282 across the 34 of their papers we have counts for
7 papers · 1 filter
Off-Policy Meta-Reinforcement Learning Based on Feature Embedding Spaces
Takahisa Imagawa, Takuya Hiraoka, Yoshimasa Tsuruoka
Meta-reinforcement learning (RL) addresses the problem of sample inefficiency in deep RL by using experience obtained in past tasks for a new task to be solved. However, most meta-…
Building a Computer Mahjong Player via Deep Convolutional Neural Networks
Shiqi Gao, Fuminori Okuya, Yoshihiro Kawahara +1
The evaluation function for imperfect information games is always hard to define but owns a significant impact on the playing strength of a program. Deep learning has made great ac…
Synthesizing Chemical Plant Operation Procedures using Knowledge, Dynamic Simulation and Deep Reinforcement Learning
Shumpei Kubosawa, Takashi Onishi, Yoshimasa Tsuruoka
Chemical plants are complex and dynamical systems consisting of many components for manipulation and sensing, whose state transitions depend on various factors such as time, distur…
Refining Manually-Designed Symbol Grounding and High-Level Planning by Policy Gradients
Takuya Hiraoka, Takashi Onishi, Takahisa Imagawa +1
Hierarchical planners that produce interpretable and appropriate plans are desired, especially in its application to supporting human decision making. In the typical development of…
Monte Carlo Tree Search with Scalable Simulation Periods for Continuously Running Tasks
Seydou Ba, Takuya Hiraoka, Takashi Onishi +2
Monte Carlo Tree Search (MCTS) is particularly adapted to domains where the potential actions can be represented as a tree of sequential decisions. For an effective action selectio…
Asymmetric Move Selection Strategies in Monte-Carlo Tree Search: Minimizing the Simple Regret at Max Nodes
Yun-Ching Liu, Yoshimasa Tsuruoka
The combination of multi-armed bandit (MAB) algorithms with Monte-Carlo tree search (MCTS) has made a significant impact in various research fields. The UCT algorithm, which combin…
Adapting Improved Upper Confidence Bounds for Monte-Carlo Tree Search
Yun-Ching Liu, Yoshimasa Tsuruoka
The UCT algorithm, which combines the UCB algorithm and Monte-Carlo Tree Search (MCTS), is currently the most widely used variant of MCTS. Recently, a number of investigations into…