Imitation learning is a class of promising policy learning algorithms that is free from many
practical issues with reinforcement learning, such as the reward design issue and the …

Abstract Model-based reinforcement learning (RL) often achieves higher sample efficiency
in practice than model-free RL by learning a dynamics model to generate samples for policy …

Transformer has achieved great successes in learning vision and language representation,
which is general across various downstream tasks. In visual control, learning transferable …

Abstract Partially Observable Markov Decision Process (POMDP) provides a principled and
generic framework to model real world sequential decision making processes but yet …

Compared to model-free reinforcement learning (RL), model-based RL is often more sample
efficient by leveraging a learned dynamics model to help decision making. However, the …

Dual-arm robots offer enhanced versatility and efficiency over single-arm counterparts by
enabling concurrent manipulation of multiple objects or cooperative execution of tasks using …

The Overfitted Brain hypothesis suggests dreams happen to allow generalization in the
human brain. Here, we ask if the same is true for reinforcement learning agents as well …

多様な状態予測によるモデルベース強化学習の改善 Page 1 G-23 2023 年度情報処理学会関西支部
支部大会 多様な状態予測によるモデルベース強化学習の改善 Improving Model-based …