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Exploring the limits of hierarchical world models in reinforcement learning
Hierarchical model-based reinforcement learning (HMBRL) aims to combine the sample
efficiency of model-based reinforcement learning with the abstraction capability of …
efficiency of model-based reinforcement learning with the abstraction capability of …
Learn A Flexible Exploration Model for Parameterized Action Markov Decision Processes
Z Wang, B Wang, M Shao, H Dou, B Tao - arxiv preprint arxiv:2501.02774, 2025 - arxiv.org
Hybrid action models are widely considered an effective approach to reinforcement learning
(RL) modeling. The current mainstream method is to train agents under Parameterized …
(RL) modeling. The current mainstream method is to train agents under Parameterized …
Generate explorative goals with large language model guidance
X Yuan, YAN ZHENG, F Zhang, D ZHANG, H Mao… - openreview.net
Reinforcement learning (RL) struggles with sparse reward environments. Recent
developments in intrinsic motivation have revealed the potential of language models to …
developments in intrinsic motivation have revealed the potential of language models to …
Nonmyopic Bayesian Optimization in Dynamic Cost Settings
Bayesian optimization (BO) is a popular framework for optimizing black-box functions,
leveraging probabilistic models such as Gaussian processes. However, conventional BO …
leveraging probabilistic models such as Gaussian processes. However, conventional BO …
Navigation with QPHIL: Offline Goal-Conditioned RL in a Learned Discretized Space
Offline Reinforcement Learning (RL) has emerged as a powerful alternative to imitation
learning for behavior modeling in various domains, particularly in complex navigation tasks …
learning for behavior modeling in various domains, particularly in complex navigation tasks …
World-Model based Hierarchical Planning with Semantic Communications for Autonomous Driving
World-model (WM) is a highly promising approach for training AI agents. However, in
complex learning systems such as autonomous driving, AI agents interact with others in a …
complex learning systems such as autonomous driving, AI agents interact with others in a …