9 citations · 12 across the 3 of their papers we have counts for
6 papers
Multi-View Dreaming: Multi-View World Model with Contrastive Learning
Akira Kinose, Masashi Okada, Ryo Okumura +1
In this paper, we propose Multi-View Dreaming, a novel reinforcement learning agent for integrated recognition and control from multi-view observations by extending Dreaming. Most…
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…
Multi-person Pose Tracking using Sequential Monte Carlo with Probabilistic Neural Pose Predictor
Masashi Okada, Shinji Takenaka, Tadahiro Taniguchi
It is an effective strategy for the multi-person pose tracking task in videos to employ prediction and pose matching in a frame-by-frame manner. For this type of approach, uncertai…
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…