Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
A Simple Decentralized Cross-Entropy Method
Cross-Entropy Method (CEM) is commonly used for planning in model-based reinforcement learning (MBRL) where a centralized approach is typically utilized to update the sampling distribution based on only the top-$k$ opera…
continuous-controlContinuous ControlModel-based Reinforcement LearningMoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations
Poor sample efficiency continues to be the primary challenge for deployment of deep Reinforcement Learning (RL) algorithms for real-world applications, and in particular for visuo-motor control. Model-based RL has the po…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Physics-Informed Model-Based Reinforcement Learning
We apply reinforcement learning (RL) to robotics tasks. One of the drawbacks of traditional RL algorithms has been their poor sample efficiency. One approach to improve the sample efficiency is model-based RL. In our mod…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1The Effectiveness of World Models for Continual Reinforcement Learning
World models power some of the most efficient reinforcement learning algorithms. In this work, we showcase that they can be harnessed for continual learning - a situation when the agent faces changing environments. World…
Continual LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1A Data-driven Pricing Scheme for Optimal Routing through Artificial Currencies
Mobility systems often suffer from a high price of anarchy due to the uncontrolled behavior of selfish users. This may result in societal costs that are significantly higher compared to what could be achieved by a centra…
ARCFairnessModel-based Reinforcement LearningPrototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning
The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse environments with unseen dynamics remai…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Offline Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity
In this work, we study a natural nonparametric estimator of the transition probability matrices of a finite controlled Markov chain. We consider an offline setting with a fixed dataset, collected using a so-called loggin…
Model-based Reinforcement LearningThe Benefits of Model-Based Generalization in Reinforcement Learning
Model-Based Reinforcement Learning (RL) is widely believed to have the potential to improve sample efficiency by allowing an agent to synthesize large amounts of imagined experience. Experience Replay (ER) can be conside…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Learning safety in model-based Reinforcement Learning using MPC and Gaussian Processes
We propose a method to encourage safety in Model Predictive Control (MPC)-based Reinforcement Learning (RL) via Gaussian Process (GP) regression. This framework consists of 1) a parametric MPC scheme that is employed as …
Gaussian ProcessesModel-based Reinforcement LearningModel Predictive Controlreinforcement-learning+1Contrastive Value Learning: Implicit Models for Simple Offline RL
Model-based reinforcement learning (RL) methods are appealing in the offline setting because they allow an agent to reason about the consequences of actions without interacting with the environment. Prior methods learn a…
continuous-controlContinuous ControlModel-based Reinforcement LearningOffline RL+1Model-based Reinforcement Learning with a Hamiltonian Canonical ODE Network
Model-based reinforcement learning usually suffers from a high sample complexity in training the world model, especially for the environments with complex dynamics. To make the training for general physical environments …
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)SAM-RL: Sensing-Aware Model-Based Reinforcement Learning via Differentiable Physics-Based Simulation and Rendering
Model-based reinforcement learning (MBRL) is recognized with the potential to be significantly more sample-efficient than model-free RL. How an accurate model can be developed automatically and efficiently from raw senso…
Deformable Object ManipulationModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Active Exploration for Robotic Manipulation
Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning in recent years. One of the key challenges in manipulation is the exploration of the dynamics of the…
Model-based Reinforcement LearningModel Predictive ControlEpistemic Monte Carlo Tree Search
The AlphaZero/MuZero (A/MZ) family of algorithms has achieved remarkable success across various challenging domains by integrating Monte Carlo Tree Search (MCTS) with learned models. Learned models introduce epistemic un…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement Learning
Reinforcement Learning (RL) algorithms can solve challenging control problems directly from image observations, but they often require millions of environment interactions to do so. Recently, model-based RL algorithms ha…
Atari Games 100kModel-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning
Improved state space models, such as Recurrent State Space Models (RSSMs), are a key factor behind recent advances in model-based reinforcement learning (RL). Yet, despite their empirical success, many of the underlying …
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Sensor Fusion+1When to Update Your Model: Constrained Model-based Reinforcement Learning
Designing and analyzing model-based RL (MBRL) algorithms with guaranteed monotonic improvement has been challenging, mainly due to the interdependence between policy optimization and model learning. Existing discrepancy …
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate
Safe reinforcement learning (RL) that solves constraint-satisfactory policies provides a promising way to the broader safety-critical applications of RL in real-world problems such as robotics. Among all safe RL approach…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Safe Reinforcement LearningOutput Feedback Adaptive Optimal Control of Affine Nonlinear systems with a Linear Measurement Model
Real-world control applications in complex and uncertain environments require adaptability to handle model uncertainties and robustness against disturbances. This paper presents an online, output-feedback, critic-only, m…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)A Unified Framework for Alternating Offline Model Training and Policy Learning
In offline model-based reinforcement learning (offline MBRL), we learn a dynamic model from historically collected data, and subsequently utilize the learned model and fixed datasets for policy learning, without further …
continuous-controlContinuous ControlModel-based Reinforcement Learningreinforcement-learning+2