Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Between Rate-Distortion Theory & Value Equivalence in Model-Based Reinforcement Learning
The quintessential model-based reinforcement-learning agent iteratively refines its estimates or prior beliefs about the true underlying model of the environment. Recent empirical successes in model-based reinforcement l…
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+2Offline Reinforcement Learning with Causal Structured World Models
Model-based methods have recently shown promising for offline reinforcement learning (RL), aiming to learn good policies from historical data without interacting with the environment. Previous model-based offline RL meth…
Model-based Reinforcement LearningOffline RLreinforcement-learningReinforcement Learning+1Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning
Model-based approaches to reinforcement learning (MBRL) exhibit favorable performance in practice, but their theoretical guarantees in large spaces are mostly restricted to the setting when transition model is Gaussian o…
continuous-controlContinuous ControlModel-based Reinforcement Learningreinforcement-learning+1Stock Trading Optimization through Model-based Reinforcement Learning with Resistance Support Relative Strength
Reinforcement learning (RL) is gaining attention by more and more researchers in quantitative finance as the agent-environment interaction framework is aligned with decision making process in many business problems. Most…
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning
A crucial challenge in reinforcement learning is to reduce the number of interactions with the environment that an agent requires to master a given task. Transfer learning proposes to address this issue by re-using knowl…
Continuous ControlModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+2Scalable Multi-Agent Model-Based Reinforcement Learning
Recent Multi-Agent Reinforcement Learning (MARL) literature has been largely focused on Centralized Training with Decentralized Execution (CTDE) paradigm. CTDE has been a dominant approach for both cooperative and mixed …
MambamodelModel-based Reinforcement LearningMulti-agent Reinforcement Learning+5Should Models Be Accurate?
Model-based Reinforcement Learning (MBRL) holds promise for data-efficiency by planning with model-generated experience in addition to learning with experience from the environment. However, in complex or changing enviro…
Meta-LearningModel-based Reinforcement LearningPlanning with Diffusion for Flexible Behavior Synthesis
Model-based reinforcement learning methods often use learning only for the purpose of estimating an approximate dynamics model, offloading the rest of the decision-making work to classical trajectory optimizers. While co…
Decision MakingDenoisingImage InpaintingModel-based Reinforcement LearningTowards biologically plausible Dreaming and Planning in recurrent spiking networks
Humans and animals can learn new skills after practicing for a few hours, while current reinforcement learning algorithms require a large amount of data to achieve good performances. Recent model-based approaches show pr…
Autonomous DrivingModel-based Reinforcement LearningOpen-Ended Question AnsweringReinforcement Learning (RL)Accelerated Reinforcement Learning for Temporal Logic Control Objectives
This paper addresses the problem of learning control policies for mobile robots, modeled as unknown Markov Decision Processes (MDPs), that are tasked with temporal logic missions, such as sequencing, coverage, or surveil…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)RLFlow: Optimising Neural Network Subgraph Transformation with World Models
Training deep learning models takes an extremely long execution time and consumes large amounts of computing resources. At the same time, recent research proposed systems and compilers that are expected to decrease deep …
Deep LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
In recent years, a growing number of deep model-based reinforcement learning (RL) methods have been introduced. The interest in deep model-based RL is not surprising, given its many potential benefits, such as higher sam…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Learning Sequential Latent Variable Models from Multimodal Time Series Data
Sequential modelling of high-dimensional data is an important problem that appears in many domains including model-based reinforcement learning and dynamics identification for control. Latent variable models applied to s…
Model-based Reinforcement LearningTime SeriesTime Series AnalysisINFOrmation Prioritization through EmPOWERment in Visual Model-Based RL
Model-based reinforcement learning (RL) algorithms designed for handling complex visual observations typically learn some sort of latent state representation, either explicitly or implicitly. Standard methods of this sor…
Model-based Reinforcement LearningReinforcement Learning (RL)Online No-regret Model-Based Meta RL for Personalized Navigation
The interaction between a vehicle navigation system and the driver of the vehicle can be formulated as a model-based reinforcement learning problem, where the navigation systems (agent) must quickly adapt to the characte…
Model-based Reinforcement LearningModel Predictive ControlValue Gradient weighted Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) is a sample efficient technique to obtain control policies, yet unavoidable modeling errors often lead performance deterioration. The model in MBRL is often solely fitted to reco…
modelModel-based Reinforcement LearningMuJoCoreinforcement-learning+2Safe Controller for Output Feedback Linear Systems using Model-Based Reinforcement Learning
The objective of this research is to enable safety-critical systems to simultaneously learn and execute optimal control policies in a safe manner to achieve complex autonomy. Learning optimal policies via trial and error…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Revisiting Model-based Value Expansion
Model-based value expansion methods promise to improve the quality of value function targets and, thereby, the effectiveness of value function learning. However, to date, these methods are being outperformed by Dyna-styl…
GPUmodelModel-based Reinforcement LearningInvestigating Compounding Prediction Errors in Learned Dynamics Models
Accurately predicting the consequences of agents' actions is a key prerequisite for planning in robotic control. Model-based reinforcement learning (MBRL) is one paradigm which relies on the iterative learning and predic…
Model-based Reinforcement LearningPredictionSAGE: Generating Symbolic Goals for Myopic Models in Deep Reinforcement Learning
Model-based reinforcement learning algorithms are typically more sample efficient than their model-free counterparts, especially in sparse reward problems. Unfortunately, many interesting domains are too complex to speci…
Deep Reinforcement LearningMinecraftModel-based Reinforcement Learningreinforcement-learning+1