5 citations · 5 across the 5 of their papers we have counts for
5 papers
Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying
Soichiro Nishimori, Paavo Parmas, Sotetsu Koyamada +4
In reinforcement learning (RL), agents benefit from exploration only because they repeatedly encounter similar states: trying different actions can improve performance or reduce un…
A Simple, Solid, and Reproducible Baseline for Bridge Bidding AI
Haruka Kita, Sotetsu Koyamada, Yotaro Yamaguchi +1
Contract bridge, a cooperative game characterized by imperfect information and multi-agent dynamics, poses significant challenges and serves as a critical benchmark in artificial i…
A Batch Sequential Halving Algorithm without Performance Degradation
Sotetsu Koyamada, Soichiro Nishimori, Shin Ishii
In this paper, we investigate the problem of pure exploration in the context of multi-armed bandits, with a specific focus on scenarios where arms are pulled in fixed-size batches.…
End-to-End Policy Gradient Method for POMDPs and Explainable Agents
Soichiro Nishimori, Sotetsu Koyamada, Shin Ishii
Real-world decision-making problems are often partially observable, and many can be formulated as a Partially Observable Markov Decision Process (POMDP). When we apply reinforcemen…
Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning
Sotetsu Koyamada, Shinri Okano, Soichiro Nishimori +4
We propose Pgx, a suite of board game reinforcement learning (RL) environments written in JAX and optimized for GPU/TPU accelerators. By leveraging JAX's auto-vectorization and par…