Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Learning Disentangled Discrete Representations
Recent successes in image generation, model-based reinforcement learning, and text-to-image generation have demonstrated the empirical advantages of discrete latent representations, although the reasons behind their bene…
Image GenerationModel-based Reinforcement LearningModel SelectionText to Image Generation+1Mode-constrained Model-based Reinforcement Learning via Gaussian Processes
Model-based reinforcement learning (RL) algorithms do not typically consider environments with multiple dynamic modes, where it is beneficial to avoid inoperable or undesirable modes. We present a model-based RL algorith…
Gaussian ProcessesModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Theoretically Guaranteed Policy Improvement Distilled from Model-Based Planning
Model-based reinforcement learning (RL) has demonstrated remarkable successes on a range of continuous control tasks due to its high sample efficiency. To save the computation cost of conducting planning online, recent p…
continuous-controlContinuous ControlModel-based Reinforcement LearningMuJoCo+1Image Transformation Sequence Retrieval with General Reinforcement Learning
In this work, the novel Image Transformation Sequence Retrieval (ITSR) task is presented, in which a model must retrieve the sequence of transformations between two given images that act as source and target, respectivel…
General Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Facing Off World Model Backbones: RNNs, Transformers, and S4
World models are a fundamental component in model-based reinforcement learning (MBRL). To perform temporally extended and consistent simulations of the future in partially observable environments, world models need to po…
Model-based Reinforcement LearningSurge Routing: Event-informed Multiagent Reinforcement Learning for Autonomous Rideshare
Large events such as conferences, concerts and sports games, often cause surges in demand for ride services that are not captured in average demand patterns, posing unique challenges for routing algorithms. We propose a …
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning$λ$-models: Effective Decision-Aware Reinforcement Learning with Latent Models
The idea of decision-aware model learning, that models should be accurate where it matters for decision-making, has gained prominence in model-based reinforcement learning. While promising theoretical results have been e…
continuous-controlContinuous ControlDecision MakingModel-based Reinforcement Learning+2Curious Replay for Model-based Adaptation
Agents must be able to adapt quickly as an environment changes. We find that existing model-based reinforcement learning agents are unable to do this well, in part because of how they use past experiences to train their …
modelModel-based Reinforcement LearningDeep Generative Models for Decision-Making and Control
Deep model-based reinforcement learning methods offer a conceptually simple approach to the decision-making and control problem: use learning for the purpose of estimating an approximate dynamics model, and offload the r…
Decision MakingImage InpaintingModel-based Reinforcement Learningreinforcement-learning+1How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations
We present a framework and algorithms to learn controlled dynamics models using neural stochastic differential equations (SDEs) -- SDEs whose drift and diffusion terms are both parametrized by neural networks. We constru…
Inductive BiasModel-based Reinforcement LearningModel Predictive ControlModel-Based Reinforcement Learning with Multi-Task Offline Pretraining
Pretraining reinforcement learning (RL) models on offline datasets is a promising way to improve their training efficiency in online tasks, but challenging due to the inherent mismatch in dynamics and behaviors across va…
Knowledge DistillationModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control
Model-based reinforcement learning (RL) has shown great promise due to its sample efficiency, but still struggles with long-horizon sparse-reward tasks, especially in offline settings where the agent learns from a fixed …
D4RLModel-based Reinforcement LearningModel Predictive ControlOffline RL+2What model does MuZero learn?
Model-based reinforcement learning (MBRL) has drawn considerable interest in recent years, given its promise to improve sample efficiency. Moreover, when using deep-learned models, it is possible to learn compact and gen…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement LearningPre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learning
Unsupervised pre-training methods utilizing large and diverse datasets have achieved tremendous success across a range of domains. Recent work has investigated such unsupervised pre-training methods for model-based reinf…
Autonomous DrivingDecoderModel-based Reinforcement LearningTransfer Learning+2Adaptive PD Control using Deep Reinforcement Learning for Local-Remote Teleoperation with Stochastic Time Delays
Local-remote systems allow robots to execute complex tasks in hazardous environments such as space and nuclear power stations. However, establishing accurate positional mapping between local and remote devices can be dif…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement LearningDigital Twin-Based 3D Map Management for Edge-Assisted Mobile Augmented Reality
In this paper, we design a 3D map management scheme for edge-assisted mobile augmented reality (MAR) to support the pose estimation of individual MAR device, which uploads camera frames to an edge server. Our objective i…
ManagementModel-based Reinforcement LearningPose EstimationTOM: Learning Policy-Aware Models for Model-Based Reinforcement Learning via Transition Occupancy Matching
Standard model-based reinforcement learning (MBRL) approaches fit a transition model of the environment to all past experience, but this wastes model capacity on data that is irrelevant for policy improvement. We instead…
Model-based Reinforcement LearningMuJoCoreinforcement-learningReinforcement LearningBridging Active Exploration and Uncertainty-Aware Deployment Using Probabilistic Ensemble Neural Network Dynamics
In recent years, learning-based control in robotics has gained significant attention due to its capability to address complex tasks in real-world environments. With the advances in machine learning algorithms and computa…
Autonomous VehiclesModel-based Reinforcement LearningSense, Imagine, Act: Multimodal Perception Improves Model-Based Reinforcement Learning for Head-to-Head Autonomous Racing
Model-based reinforcement learning (MBRL) techniques have recently yielded promising results for real-world autonomous racing using high-dimensional observations. MBRL agents, such as Dreamer, solve long-horizon tasks by…
Autonomous Racingcontinuous-controlContinuous ControlModel-based Reinforcement Learning+2A Survey on Offline Model-Based Reinforcement Learning
Model-based approaches are becoming increasingly popular in the field of offline reinforcement learning, with high potential in real-world applications due to the model's capability of thoroughly utilizing the large hist…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1