Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representations
In this work, we present MoConVQ, a novel unified framework for physics-based motion control leveraging scalable discrete representations. Building upon vector quantized variational autoencoders (VQ-VAE) and model-based …
In-Context LearningModel-based Reinforcement LearningMotion GenerationSTORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning
Recently, model-based reinforcement learning algorithms have demonstrated remarkable efficacy in visual input environments. These approaches begin by constructing a parameterized simulation world model of the real enviro…
Atari Games 100kModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
Dyna-style model-based reinforcement learning contains two phases: model rollouts to generate sample for policy learning and real environment exploration using current policy for dynamics model learning. However, due to …
continuous-controlContinuous ControlModel-based Reinforcement LearningModel Predictive ControlA Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning
Model-based Reinforcement Learning (MBRL) aims to make agents more sample-efficient, adaptive, and explainable by learning an explicit model of the environment. While the capabilities of MBRL agents have significantly im…
Model-based Reinforcement LearningMulti-timestep models for Model-based Reinforcement Learning
In model-based reinforcement learning (MBRL), most algorithms rely on simulating trajectories from one-step dynamics models learned on data. A critical challenge of this approach is the compounding of one-step prediction…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learning
Learning a precise dynamics model can be crucial for offline reinforcement learning, which, unfortunately, has been found to be quite challenging. Dynamics models that are learned by fitting historical transitions often …
D4RLModel-based Reinforcement Learningreinforcement-learningReinforcement LearningLanguage Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
While language models (LMs) have shown potential across a range of decision-making tasks, their reliance on simple acting processes limits their broad deployment as autonomous agents. In this paper, we introduce Language…
Code GenerationDecision MakingHumanEvalIn-Context Learning+3Amortized Network Intervention to Steer the Excitatory Point Processes
Excitatory point processes (i.e., event flows) occurring over dynamic graphs (i.e., evolving topologies) provide a fine-grained model to capture how discrete events may spread over time and space. How to effectively stee…
Decision MakingModel-based Reinforcement LearningModel Predictive ControlPoint Processes+1Probabilistic Reach-Avoid for Bayesian Neural Networks
Model-based reinforcement learning seeks to simultaneously learn the dynamics of an unknown stochastic environment and synthesise an optimal policy for acting in it. Ensuring the safety and robustness of sequential decis…
Model-based Reinforcement LearningConsciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
Inspired by human conscious planning, we propose Skipper, a model-based reinforcement learning framework utilizing spatio-temporal abstractions to generalize better in novel situations. It automatically decomposes the gi…
Decision MakingModel-based Reinforcement Learningreinforcement-learningZero-shot GeneralizationHarmonyDream: Task Harmonization Inside World Models
Model-based reinforcement learning (MBRL) holds the promise of sample-efficient learning by utilizing a world model, which models how the environment works and typically encompasses components for two tasks: observation …
Atari Games 100kModel-based Reinforcement Learningreinforcement-learningMoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation
Robotic systems that aspire to operate in uninstrumented real-world environments must perceive the world directly via onboard sensing. Vision-based learning systems aim to eliminate the need for environment instrumentati…
Contact-rich ManipulationModel-based Reinforcement LearningRobot ManipulationState EstimationPractical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling
This paper addresses the prediction stability, prediction accuracy and control capability of the current probabilistic model-based reinforcement learning (MBRL) built on neural networks. A novel approach dropout-based pr…
Deep Reinforcement LearningModel-based Reinforcement LearningMuJoCoPredictionDOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning
Model-based reinforcement learning (RL), which learns environment model from offline dataset and generates more out-of-distribution model data, has become an effective approach to the problem of distribution shift in off…
D4RLmodelModel-based Reinforcement LearningOffline RL+3Mind the Uncertainty: Risk-Aware and Actively Exploring Model-Based Reinforcement Learning
We introduce a simple but effective method for managing risk in model-based reinforcement learning with trajectory sampling that involves probabilistic safety constraints and balancing of optimism in the face of epistemi…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningDistributionally Robust Model-based Reinforcement Learning with Large State Spaces
Three major challenges in reinforcement learning are the complex dynamical systems with large state spaces, the costly data acquisition processes, and the deviation of real-world dynamics from the training environment de…
Gaussian ProcessesModel-based Reinforcement Learningreinforcement-learningReinforcement LearningThe Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning
Due to the proliferation of malware, defenders are increasingly turning to automation and machine learning as part of the malware detection tool-chain. However, machine learning models are susceptible to adversarial atta…
Adversarial AttackClassificationMalware Detection+3RePo: Resilient Model-Based Reinforcement Learning by Regularizing Posterior Predictability
Visual model-based RL methods typically encode image observations into low-dimensional representations in a manner that does not eliminate redundant information. This leaves them susceptible to spurious variations -- cha…
Model-based Reinforcement Learningreinforcement-learningTest-time AdaptationValue-Distributional Model-Based Reinforcement Learning
Quantifying uncertainty about a policy's long-term performance is important to solve sequential decision-making tasks. We study the problem from a model-based Bayesian reinforcement learning perspective, where the goal i…
continuous-controlContinuous ControlDecision MakingDistributional Reinforcement Learning+6Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving
In recent years there have been remarkable advancements in autonomous driving. While autonomous vehicles demonstrate high performance in closed-set conditions, they encounter difficulties when confronted with unexpected …
Anomaly DetectionAutonomous DrivingAutonomous VehiclesModel-based Reinforcement Learning