Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Pre-Trained Video Generative Models as World Simulators
Video generative models pre-trained on large-scale internet datasets have achieved remarkable success, excelling at producing realistic synthetic videos. However, they often generate clips based on static prompts (e.g., …
Model-based Reinforcement LearningTD-M(PC)$^2$: Improving Temporal Difference MPC Through Policy Constraint
Model-based reinforcement learning algorithms that combine model-based planning and learned value/policy prior have gained significant recognition for their high data efficiency and superior performance in continuous con…
continuous-controlContinuous ControlModel-based Reinforcement LearningDream to Drive with Predictive Individual World Model
It is still a challenging topic to make reactive driving behaviors in complex urban environments as road users' intentions are unknown. Model-based reinforcement learning (MBRL) offers great potential to learn a reactive…
Autonomous DrivingmodelModel-based Reinforcement Learningreinforcement-learning+3On Rollouts in Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) seeks to enhance data efficiency by learning a model of the environment and generating synthetic rollouts from it. However, accumulated model errors during these rollouts can dis…
modelModel-based Reinforcement LearningMuJoCoreinforcement-learning+1Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking
Lyapunov optimization theory has recently emerged as a powerful mathematical framework for solving complex stochastic optimization problems by transforming long-term objectives into a sequence of real-time short-term dec…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningStochastic OptimizationObjects matter: object-centric world models improve reinforcement learning in visually complex environments
Deep reinforcement learning has achieved remarkable success in learning control policies from pixels across a wide range of tasks, yet its application remains hindered by low sample efficiency, requiring significantly mo…
Atari GamesDeep Reinforcement LearningModel-based Reinforcement LearningObjectAdaWM: Adaptive World Model based Planning for Autonomous Driving
World model based reinforcement learning (RL) has emerged as a promising approach for autonomous driving, which learns a latent dynamics model and uses it to train a planning policy. To speed up the learning process, the…
Autonomous DrivingModel-based Reinforcement LearningReinforcement Learning (RL)GLAM: Global-Local Variation Awareness in Mamba-based World Model
Mimicking the real interaction trajectory in the inference of the world model has been shown to improve the sample efficiency of model-based reinforcement learning (MBRL) algorithms. Many methods directly use known state…
MambaModel-based Reinforcement LearningRobotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics
Learning robust and generalizable world models is crucial for enabling efficient and scalable robotic control in real-world environments. In this work, we introduce a novel framework for learning world models that accura…
Model-based Reinforcement LearningEVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning
Posterior Sampling for Reinforcement Learning (PSRL) is a well-known algorithm that augments model-based reinforcement learning (MBRL) algorithms with Thompson sampling. PSRL maintains posterior distributions of the envi…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningThompson SamplingApproximating Fixpoints of Approximated Functions
Fixpoints are ubiquitous in computer science and when dealing with quantitative semantics and verification one often considers least fixpoints of (higher-dimensional) functions over the non-negative reals. We show how to…
Model-based Reinforcement LearningInferring Transition Dynamics from Value Functions
In reinforcement learning, the value function is typically trained to solve the Bellman equation, which connects the current value to future values. This temporal dependency hints that the value function may contain impl…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningA Reduced Order Iterative Linear Quadratic Regulator (ILQR) Technique for the Optimal Control of Nonlinear Partial Differential Equations
In this paper, we introduce a reduced order model-based reinforcement learning (MBRL) approach, utilizing the Iterative Linear Quadratic Regulator (ILQR) algorithm for the optimal control of nonlinear partial differentia…
Model-based Reinforcement LearningKnowledge Transfer in Model-Based Reinforcement Learning Agents for Efficient Multi-Task Learning
We propose an efficient knowledge transfer approach for model-based reinforcement learning, addressing the challenge of deploying large world models in resource-constrained environments. Our method distills a high-capaci…
Model-based Reinforcement LearningMulti-Task LearningQuantizationreinforcement-learning+2Digital Twin Calibration with Model-Based Reinforcement Learning
This paper presents a novel methodological framework, called the Actor-Simulator, that incorporates the calibration of digital twins into model-based reinforcement learning for more effective control of stochastic system…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement LearningDiminishing Return of Value Expansion Methods
Model-based reinforcement learning aims to increase sample efficiency, but the accuracy of dynamics models and the resulting compounding errors are often seen as key limitations. This paper empirically investigates poten…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningStealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning
Dyna-style off-policy model-based reinforcement learning (DMBRL) algorithms are a family of techniques for generating synthetic state transition data and thereby enhancing the sample efficiency of off-policy RL algorithm…
Model-based Reinforcement LearningOpenAI GymLearning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps
Learning navigation capabilities in different environments has long been one of the major challenges in decision-making. In this work, we focus on zero-shot navigation ability using given abstract $2$-D top-down maps. Li…
Decision MakingModel-based Reinforcement LearningMulti-Task LearningNavigateSimuDICE: Offline Policy Optimization Through World Model Updates and DICE Estimation
In offline reinforcement learning, deriving an effective policy from a pre-collected set of experiences is challenging due to the distribution mismatch between the target policy and the behavioral policy used to collect …
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningPolicy-shaped prediction: avoiding distractions in model-based reinforcement learning
Model-based reinforcement learning (MBRL) is a promising route to sample-efficient policy optimization. However, a known vulnerability of reconstruction-based MBRL consists of scenarios in which detailed aspects of the w…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning