39 citations · 133 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
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…