Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Data-driven optimal control of unknown nonlinear dynamical systems using the Koopman operator
Nonlinear optimal control is vital for numerous applications but remains challenging for unknown systems due to the difficulties in accurately modelling dynamics and handling computational demands, particularly in high-d…
Data Driven Optimal ControlModel-based Reinforcement LearningStructure learning with Temporal Gaussian Mixture for model-based Reinforcement Learning
Model-based reinforcement learning refers to a set of approaches capable of sample-efficient decision making, which create an explicit model of the environment. This model can subsequently be used for learning optimal po…
Decision MakingModel-based Reinforcement LearningNavigateQ-Learning+1The Surprising Ineffectiveness of Pre-Trained Visual Representations for Model-Based Reinforcement Learning
Visual Reinforcement Learning (RL) methods often require extensive amounts of data. As opposed to model-free RL, model-based RL (MBRL) offers a potential solution with efficient data utilization through planning. Additio…
DiversityModel-based Reinforcement LearningReinforcement Learning (RL)Imagine-2-Drive: Leveraging High-Fidelity World Models via Multi-Modal Diffusion Policies
World Model-based Reinforcement Learning (WMRL) enables sample efficient policy learning by reducing the need for online interactions which can potentially be costly and unsafe, especially for autonomous driving. However…
Autonomous DrivingAutonomous VehiclesDenoisingFuture prediction+2Inverse Transition Learning: Learning Dynamics from Demonstrations
We consider the problem of estimating the transition dynamics $T^*$ from near-optimal expert trajectories in the context of offline model-based reinforcement learning. We develop a novel constraint-based method, Inverse …
Decision MakingManagementModel-based Reinforcement LearningWhen to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter
Reinforcement learning (RL) algorithms can be divided into two classes: model-free algorithms, which are sample-inefficient, and model-based algorithms, which suffer from model bias. Dyna-style algorithms combine these t…
Model-based Reinforcement LearningReinforcement Learning (RL)Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning
Offline RL is a powerful approach for data-driven decision-making and control. Compared to model-free methods, offline model-based RL (MBRL) explicitly learns world models from a static dataset and uses them as surrogate…
D4RLModel-based Reinforcement LearningMuJoCoOffline RL+3Zero-shot Model-based Reinforcement Learning using Large Language Models
The emerging zero-shot capabilities of Large Language Models (LLMs) have led to their applications in areas extending well beyond natural language processing tasks. In reinforcement learning, while LLMs have been extensi…
In-Context LearningModel-based Reinforcement Learningreinforcement-learningReinforcement LearningDODT: Enhanced Online Decision Transformer Learning through Dreamer's Actor-Critic Trajectory Forecasting
Advancements in reinforcement learning have led to the development of sophisticated models capable of learning complex decision-making tasks. However, efficiently integrating world models with decision transformers remai…
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Make the Pertinent Salient: Task-Relevant Reconstruction for Visual Control with Distractions
Recent advancements in Model-Based Reinforcement Learning (MBRL) have made it a powerful tool for visual control tasks. Despite improved data efficiency, it remains challenging to train MBRL agents with generalizable per…
Model-based Reinforcement LearningRepresentation LearningDrama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient
Model-based reinforcement learning (RL) offers a solution to the data inefficiency that plagues most model-free RL algorithms. However, learning a robust world model often requires complex and deep architectures, which a…
MambaModel-based Reinforcement LearningReinforcement Learning (RL)SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels
Learning a latent dynamics model provides a task-agnostic representation of an agent's understanding of its environment. Leveraging this knowledge for model-based reinforcement learning (RL) holds the potential to improv…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Learning to Walk from Three Minutes of Real-World Data with Semi-structured Dynamics Models
Traditionally, model-based reinforcement learning (MBRL) methods exploit neural networks as flexible function approximators to represent $\textit{a priori}$ unknown environment dynamics. However, training data are typica…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningEfficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling
Learning complex robot behavior through interactions with the environment necessitates principled exploration. Effective strategies should prioritize exploring regions of the state-action space that maximize rewards, wit…
continuous-controlContinuous ControlModel-based Reinforcement LearningMuJoCo+3Bisimulation metric for Model Predictive Control
Model-based reinforcement learning has shown promise for improving sample efficiency and decision-making in complex environments. However, existing methods face challenges in training stability, robustness to noise, and …
Computational Efficiencycontinuous-controlContinuous ControlDecision Making+3Model-Free versus Model-Based Reinforcement Learning for Fixed-Wing UAV Attitude Control Under Varying Wind Conditions
This paper evaluates and compares the performance of model-free and model-based reinforcement learning for the attitude control of fixed-wing unmanned aerial vehicles using PID as a reference point. The comparison focuse…
modelModel-based Reinforcement LearningModel Predictive Controlreinforcement-learning+1Learning Discrete World Models for Heuristic Search
For many sequential decision making problems, planning is often necessary to find solutions. However, for domains such as those encountered in robotics, the transition function, also known as the world model, is often un…
Deep Reinforcement LearningHeuristic SearchModel-based Reinforcement LearningReinforcement Learning+4CoLaNET -- A Spiking Neural Network with Columnar Layered Architecture for Classification
In the present paper, I describe a spiking neural network (SNN) architecture which, can be used in wide range of supervised learning classification tasks. It is assumed, that all participating signals (the classified obj…
Model-based Reinforcement LearningTraffic expertise meets residual RL: Knowledge-informed model-based residual reinforcement learning for CAV trajectory control
Model-based reinforcement learning (RL) is anticipated to exhibit higher sample efficiency compared to model-free RL by utilizing a virtual environment model. However, it is challenging to obtain sufficiently accurate re…
Model-based Reinforcement LearningReinforcement Learning (RL)Enhancing Analogical Reasoning in the Abstraction and Reasoning Corpus via Model-Based RL
This paper demonstrates that model-based reinforcement learning (model-based RL) is a suitable approach for the task of analogical reasoning. We hypothesize that model-based RL can solve analogical reasoning tasks more e…
ARCModel-based Reinforcement Learning