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cs.AI2023
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…
cs.AI2023★ 5 cited
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…