Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
TransDreamerV3: Implanting Transformer In DreamerV3
This paper introduces TransDreamerV3, a reinforcement learning model that enhances the DreamerV3 architecture by integrating a transformer encoder. The model is designed to improve memory and decision-making capabilities…
Decision MakingMinecraftModel-based Reinforcement Learningreinforcement-learning+1On Quantum BSDE Solver for High-Dimensional Parabolic PDEs
We propose a quantum machine learning framework for approximating solutions to high-dimensional parabolic partial differential equations (PDEs) that can be reformulated as backward stochastic differential equations (BSDE…
Model-based Reinforcement LearningQuantum Machine LearningRelative Entropy Regularized Reinforcement Learning for Efficient Encrypted Policy Synthesis
We propose an efficient encrypted policy synthesis to develop privacy-preserving model-based reinforcement learning. We first demonstrate that the relative-entropy-regularized reinforcement learning framework offers a co…
Model-based Reinforcement LearningPrivacy PreservingQuantizationreinforcement-learning+1Accelerating Model-Based Reinforcement Learning using Non-Linear Trajectory Optimization
This paper addresses the slow policy optimization convergence of Monte Carlo Probabilistic Inference for Learning Control (MC-PILCO), a state-of-the-art model-based reinforcement learning (MBRL) algorithm, by integrating…
Model-based Reinforcement LearningBregman Centroid Guided Cross-Entropy Method
The Cross-Entropy Method (CEM) is a widely adopted trajectory optimizer in model-based reinforcement learning (MBRL), but its unimodal sampling strategy often leads to premature convergence in multimodal landscapes. In t…
DiversityModel-based Reinforcement LearningWorld Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks
World models are emerging as a transformative paradigm in artificial intelligence, enabling agents to construct internal representations of their environments for predictive reasoning, planning, and decision-making. By l…
Decision MakingModel-based Reinforcement LearningTrajectory PlanningCalibrated Value-Aware Model Learning with Stochastic Environment Models
The idea of value-aware model learning, that models should produce accurate value estimates, has gained prominence in model-based reinforcement learning. The MuZero loss, which penalizes a model's value function predicti…
Model-based Reinforcement LearningJEDI: Latent End-to-end Diffusion Mitigates Agent-Human Performance Asymmetry in Model-Based Reinforcement Learning
Recent advances in model-based reinforcement learning (MBRL) have achieved super-human level performance on the Atari100k benchmark, driven by reinforcement learning agents trained on powerful diffusion world models. How…
Model-based Reinforcement LearningMedDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support
Timely and personalized treatment decisions are essential across a wide range of healthcare settings where patient responses vary significantly and evolve over time. Clinical data used to support these decisions are ofte…
ImputationModel-based Reinforcement LearningRecommendation SystemsReinforcement Learning (RL)Deep Active Inference Agents for Delayed and Long-Horizon Environments
With the recent success of world-model agents, which extend the core idea of model-based reinforcement learning by learning a differentiable model for sample-efficient control across diverse tasks, active inference (AIF)…
Model-based Reinforcement LearningRaw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)
Reinforcement Learning (RL) can mitigate the causal confusion and distribution shift inherent to imitation learning (IL). However, applying RL to end-to-end autonomous driving (E2E-AD) remains an open problem for its tra…
Autonomous DrivingBench2DriveCARLA Leaderboard 2.0Imitation Learning+4Gaze Into the Abyss -- Planning to Seek Entropy When Reward is Scarce
Model-based reinforcement learning (MBRL) offers an intuitive way to increase the sample efficiency of model-free RL methods by simultaneously training a world model that learns to predict the future. MBRL methods have p…
Model-based Reinforcement LearningModel Predictive ControlImproving planning and MBRL with temporally-extended actions
Continuous time systems are often modeled using discrete time dynamics but this requires a small simulation step to maintain accuracy. In turn, this requires a large planning horizon which leads to computationally demand…
Model-based Reinforcement LearningTemporal Distance-aware Transition Augmentation for Offline Model-based Reinforcement Learning
The goal of offline reinforcement learning (RL) is to extract a high-performance policy from the fixed datasets, minimizing performance degradation due to out-of-distribution (OOD) samples. Offline model-based RL (MBRL) …
D4RLModel-based Reinforcement LearningReinforcement Learning (RL)Policy-Driven World Model Adaptation for Robust Offline Model-based Reinforcement Learning
Offline reinforcement learning (RL) offers a powerful paradigm for data-driven control. Compared to model-free approaches, offline model-based RL (MBRL) explicitly learns a world model from a static dataset and uses it a…
D4RLmodelModel-based Reinforcement LearningMuJoCo+1Multi-Goal Dexterous Hand Manipulation using Probabilistic Model-based Reinforcement Learning
This paper tackles the challenge of learning multi-goal dexterous hand manipulation tasks using model-based Reinforcement Learning. We propose Goal-Conditioned Probabilistic Model Predictive Control (GC-PMPC) by designin…
Model-based Reinforcement LearningModel Predictive ControlPIN-WM: Learning Physics-INformed World Models for Non-Prehensile Manipulation
While non-prehensile manipulation (e.g., controlled pushing/poking) constitutes a foundational robotic skill, its learning remains challenging due to the high sensitivity to complex physical interactions involving fricti…
FrictionModel-based Reinforcement LearningState EstimationData-Assimilated Model-Based Reinforcement Learning for Partially Observed Chaotic Flows
The goal of many applications in energy and transport sectors is to control turbulent flows. However, because of chaotic dynamics and high dimensionality, the control of turbulent flows is exceedingly difficult. Model-fr…
Model-based Reinforcement LearningReinforcement Learning (RL)State EstimationLearning global control of underactuated systems with Model-Based Reinforcement Learning
This short paper describes our proposed solution for the third edition of the "AI Olympics with RealAIGym" competition, held at ICRA 2025. We employed Monte-Carlo Probabilistic Inference for Learning Control (MC-PILCO), …
AcrobotModel-based Reinforcement LearningProbabilistic Pontryagin's Maximum Principle for Continuous-Time Model-Based Reinforcement Learning
Without exact knowledge of the true system dynamics, optimal control of non-linear continuous-time systems requires careful treatment of epistemic uncertainty. In this work, we propose a probabilistic extension to Pontry…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning