Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows
The intrinsic high dimension of fluid dynamics is an inherent challenge to control of aerodynamic flows, and this is further complicated by a flow's nonlinear response to strong disturbances. Deep reinforcement learning,…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Offline Model-Based Reinforcement Learning with Anti-Exploration
Model-based reinforcement learning (MBRL) algorithms learn a dynamics model from collected data and apply it to generate synthetic trajectories to enable faster learning. This is an especially promising paradigm in offli…
D4RLmodelModel-based Reinforcement LearningOffline RL+3Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds
Learning a transition model via Maximum Likelihood Estimation (MLE) followed by planning inside the learned model is perhaps the most standard and simplest Model-based Reinforcement Learning (RL) framework. In this work,…
Model-based Reinforcement LearningOffline RLReinforcement Learning (RL)The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited
Interpretability of deep reinforcement learning systems could assist operators with understanding how they interact with their environment. Vector quantization methods -- also called codebook methods -- discretize a neur…
Deep Reinforcement LearningDisentanglementModel-based Reinforcement LearningQuantization+2Modeling flexible behavior with remapping-based hippocampal sequence learning
Animals flexibly change their behavior depending on context. It is reported that the hippocampus is one of the most prominent regions for contextual behaviors, and its sequential activity shows context dependency. Howeve…
HippocampusModel-based Reinforcement LearningModel SelectionModel-based Policy Optimization using Symbolic World Model
The application of learning-based control methods in robotics presents significant challenges. One is that model-free reinforcement learning algorithms use observation data with low sample efficiency. To address this cha…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1A Safe and Data-efficient Model-based Reinforcement Learning System for HVAC Control
Model-Based Reinforcement Learning (MBRL) has been widely studied for Heating, Ventilation, and Air Conditioning (HVAC) control in buildings. One of the critical challenges is the large amount of data required to effecti…
Model-based Reinforcement LearningBECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
Offline model-based reinforcement learning (MBRL) enhances data efficiency by utilizing pre-collected datasets to learn models and policies, especially in scenarios where exploration is costly or infeasible. Nevertheless…
Model-based Reinforcement LearningOffline RLGraph Neural Networks with Model-based Reinforcement Learning for Multi-agent Systems
Multi-agent systems (MAS) constitute a significant role in exploring machine intelligence and advanced applications. In order to deeply investigate complicated interactions within MAS scenarios, we originally propose "GN…
Autonomous DrivingModel-based Reinforcement LearningModel Predictive ControlFOSP: Fine-tuning Offline Safe Policy through World Models
Offline Safe Reinforcement Learning (RL) seeks to address safety constraints by learning from static datasets and restricting exploration. However, these approaches heavily rely on the dataset and struggle to generalize …
Model-based Reinforcement LearningOffline RLReinforcement Learning (RL)Safe Reinforcement LearningHybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach
Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape, especially with the growing integration of the Internet of Medical Things (IoMT). The rise of generati…
Deep Reinforcement LearningManagementModel-based Reinforcement LearningRAG+2Identifying Ordinary Differential Equations for Data-efficient Model-based Reinforcement Learning
The identification of a mathematical dynamics model is a crucial step in the designing process of a controller. However, it is often very difficult to identify the system's governing equations, especially in complex envi…
Model-based Reinforcement LearningPhysics-informed machine learningState and Input Constrained Output-Feedback Adaptive Optimal Control of Affine Nonlinear Systems
In this paper, a novel online, output-feedback, critic-only, model-based reinforcement learning framework is developed for safety-critical control systems operating in complex environments. The developed framework ensure…
Model-based Reinforcement LearningState EstimationBounding-Box Inference for Error-Aware Model-Based Reinforcement Learning
In model-based reinforcement learning, simulated experiences from the learned model are often treated as equivalent to experience from the real environment. However, when the model is inaccurate, it can catastrophically …
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningInfinite-Horizon Reinforcement Learning with Multinomial Logistic Function Approximation
We study model-based reinforcement learning with non-linear function approximation where the transition function of the underlying Markov decision process (MDP) is given by a multinomial logistic (MNL) model. We develop …
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningRobust Model-Based Reinforcement Learning with an Adversarial Auxiliary Model
Reinforcement learning has demonstrated impressive performance in various challenging problems such as robotics, board games, and classical arcade games. However, its real-world applications can be hindered by the absenc…
Board GamesmodelModel-based Reinforcement LearningMuJoCo+2Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation
Adaptive brain stimulation can treat neurological conditions such as Parkinson's disease and post-stroke motor deficits by influencing abnormal neural activity. Because of patient heterogeneity, each patient requires a u…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement learning-based architecture search for quantum machine learning
Quantum machine learning models use encoding circuits to map data into a quantum Hilbert space. While it is well known that the architecture of these circuits significantly influences core properties of the resulting mod…
Model-based Reinforcement LearningQuantum Machine Learningreinforcement-learningReinforcement LearningA New View on Planning in Online Reinforcement Learning
This paper investigates a new approach to model-based reinforcement learning using background planning: mixing (approximate) dynamic programming updates and model-free updates, similar to the Dyna architecture. Backgroun…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningLearning to Play Atari in a World of Tokens
Model-based reinforcement learning agents utilizing transformers have shown improved sample efficiency due to their ability to model extended context, resulting in more accurate world models. However, for complex reasoni…
DecoderModel-based Reinforcement Learning