155 citations · 765 across the 63 of their papers we have counts for
8 papers · 1 filter
Symbol Emergence as Inter-personal Categorization with Head-to-head Latent Word
Kazuma Furukawa, Akira Taniguchi, Yoshinobu Hagiwara +1
In this study, we propose a head-to-head type (H2H-type) inter-personal multimodal Dirichlet mixture (Inter-MDM) by modifying the original Inter-MDM, which is a probabilistic gener…
DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction
Masashi Okada, Tadahiro Taniguchi
The present paper proposes a novel reinforcement learning method with world models, DreamingV2, a collaborative extension of DreamerV2 and Dreaming. DreamerV2 is a cutting-edge mod…
Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction
Masashi Okada, Tadahiro Taniguchi
In the present paper, we propose a decoder-free extension of Dreamer, a leading model-based reinforcement learning (MBRL) method from pixels. Dreamer is a sample- and cost-efficien…
PlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference
Masashi Okada, Norio Kosaka, Tadahiro Taniguchi
In the present paper, we propose an extension of the Deep Planning Network (PlaNet), also referred to as PlaNet of the Bayesians (PlaNet-Bayes). There has been a growing demand in…
Domain-Adversarial and Conditional State Space Model for Imitation Learning
Ryo Okumura, Masashi Okada, Tadahiro Taniguchi
State representation learning (SRL) in partially observable Markov decision processes has been studied to learn abstract features of data useful for robot control tasks. For SRL, a…
Neuro-SERKET: Development of Integrative Cognitive System through the Composition of Deep Probabilistic Generative Models
Tadahiro Taniguchi, Tomoaki Nakamura, Masahiro Suzuki +5
This paper describes a framework for the development of an integrative cognitive system based on probabilistic generative models (PGMs) called Neuro-SERKET. Neuro-SERKET is an exte…
Variational Inference MPC for Bayesian Model-based Reinforcement Learning
Masashi Okada, Tadahiro Taniguchi
In recent studies on model-based reinforcement learning (MBRL), incorporating uncertainty in forward dynamics is a state-of-the-art strategy to enhance learning performance, making…
Integration of Imitation Learning using GAIL and Reinforcement Learning using Task-achievement Rewards via Probabilistic Graphical Model
Akira Kinose, Tadahiro Taniguchi
Integration of reinforcement learning and imitation learning is an important problem that has been studied for a long time in the field of intelligent robotics. Reinforcement learn…