Search Results for author:
Found 7 papers, 2 papers with code
End-to-End Policy Gradient Method for POMDPs and Explainable Agents
We proposed an RL algorithm that estimates the hidden states by end-to-end training, and visualize the estimation as a state-transition graph.
Pgx: Hardware-Accelerated Parallel Game Simulators for Reinforcement Learning
We propose Pgx, a suite of board game reinforcement learning (RL) environments written in JAX and optimized for GPU/TPU accelerators.
Suphx: Mastering Mahjong with Deep Reinforcement Learning
Artificial Intelligence (AI) has achieved great success in many domains, and game AI is widely regarded as its beachhead since the dawn of AI.
Alpha-divergence bridges maximum likelihood and reinforcement learning in neural sequence generation
Neural sequence generation is commonly approached by using maximum- likelihood (ML) estimation or reinforcement learning (RL).
Neural Sequence Model Training via $α$-divergence Minimization
We propose a new neural sequence model training method in which the objective function is defined by $\alpha$-divergence.
Deep learning of fMRI big data: a novel approach to subject-transfer decoding
Our PSA successfully visualized the subject-independent features contributing to the subject-transferability of the trained decoder.
Principal Sensitivity Analysis
We then visualize the PSMs to demonstrate the PSA's ability to decompose the knowledge acquired by the trained classifiers.
