Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Human Machine Co-adaption Interface via Cooperation Markov Decision Process System
This paper aims to develop a new human-machine interface to improve rehabilitation performance from the perspective of both the user (patient) and the machine (robot) by introducing the co-adaption techniques via model-b…
Model-based Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learningReinforcement LearningPosterior Sampling for Deep Reinforcement Learning
Despite remarkable successes, deep reinforcement learning algorithms remain sample inefficient: they require an enormous amount of trial and error to find good policies. Model-based algorithms promise sample efficiency b…
Computational EfficiencyDeep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learning+3Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning
We propose a novel generalization of constrained Markov decision processes (CMDPs) that we call the \emph{semi-infinitely constrained Markov decision process} (SICMDP). Particularly, we consider a continuum of constraint…
Decision MakingDeep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learning+2FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems
Model-based reinforcement learning is a powerful tool, but collecting data to fit an accurate model of the system can be costly. Exploring an unknown environment in a sample-efficient manner is hence of great importance.…
Experimental DesignModel-based Reinforcement LearningSample-efficient Model-based Reinforcement Learning for Quantum Control
We propose a model-based reinforcement learning (RL) approach for noisy time-dependent gate optimization with improved sample complexity over model-free RL. Sample complexity is the number of controller interactions with…
Inductive BiasModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Model Based Reinforcement Learning for Personalized Heparin Dosing
A key challenge in sequential decision making is optimizing systems safely under partial information. While much of the literature has focused on the cases of either partially known states or partially known dynamics, it…
Decision MakingmodelModel-based Reinforcement Learningreinforcement-learning+2Decision-Focused Model-based Reinforcement Learning for Reward Transfer
Model-based reinforcement learning (MBRL) provides a way to learn a transition model of the environment, which can then be used to plan personalized policies for different patient cohorts and to understand the dynamics i…
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement LearningState and Parameter Estimation for Affine Nonlinear Systems
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 Learningparameter estimationreinforcement-learningRisk-Sensitive and Robust Model-Based Reinforcement Learning and Planning
Many sequential decision-making problems that are currently automated, such as those in manufacturing or recommender systems, operate in an environment where there is either little uncertainty, or zero risk of catastroph…
Decision MakingModel-based Reinforcement LearningRecommendation Systemsreinforcement-learning+3Model-Based Reinforcement Learning with Isolated Imaginations
World models learn the consequences of actions in vision-based interactive systems. However, in practical scenarios like autonomous driving, noncontrollable dynamics that are independent or sparsely dependent on action s…
Autonomous DrivingmodelModel-based Reinforcement Learningreinforcement-learning+3EDGI: Equivariant Diffusion for Planning with Embodied Agents
Embodied agents operate in a structured world, often solving tasks with spatial, temporal, and permutation symmetries. Most algorithms for planning and model-based reinforcement learning (MBRL) do not take this rich geom…
Model-based Reinforcement LearningA New Policy Iteration Algorithm For Reinforcement Learning in Zero-Sum Markov Games
Optimal policies in standard MDPs can be obtained using either value iteration or policy iteration. However, in the case of zero-sum Markov games, there is no efficient policy iteration algorithm; e.g., it has been shown…
Model-based Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Dynamic Update-to-Data Ratio: Minimizing World Model Overfitting
Early stopping based on the validation set performance is a popular approach to find the right balance between under- and overfitting in the context of supervised learning. However, in reinforcement learning, even for su…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning
One of the key behavioral characteristics used in neuroscience to determine whether the subject of study -- be it a rodent or a human -- exhibits model-based learning is effective adaptation to local changes in the envir…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)On the Benefits of Leveraging Structural Information in Planning Over the Learned Model
Model-based Reinforcement Learning (RL) integrates learning and planning and has received increasing attention in recent years. However, learning the model can incur a significant cost (in terms of sample complexity), du…
Model-based Reinforcement LearningReinforcement Learning (RL)Transformer-based World Models Are Happy With 100k Interactions
Deep neural networks have been successful in many reinforcement learning settings. However, compared to human learners they are overly data hungry. To build a sample-efficient world model, we apply a transformer to real-…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Predictive Experience Replay for Continual Visual Control and Forecasting
Learning physical dynamics in a series of non-stationary environments is a challenging but essential task for model-based reinforcement learning (MBRL) with visual inputs. It requires the agent to consistently adapt to n…
Continual LearningModel-based Reinforcement LearningMulti-Task LearningVideo PredictionBeware of Instantaneous Dependence in Reinforcement Learning
Playing an important role in Model-Based Reinforcement Learning (MBRL), environment models aim to predict future states based on the past. Existing works usually ignore instantaneous dependence in the state, that is, ass…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Sample-efficient Real-time Planning with Curiosity Cross-Entropy Method and Contrastive Learning
Model-based reinforcement learning (MBRL) with real-time planning has shown great potential in locomotion and manipulation control tasks. However, the existing planning methods, such as the Cross-Entropy Method (CEM), do…
continuous-controlContinuous ControlContrastive LearningModel-based Reinforcement Learning+1Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning
Model-based reinforcement learning is one approach to increase sample efficiency. However, the accuracy of the dynamics model and the resulting compounding error over modelled trajectories are commonly regarded as key li…
continuous-controlContinuous ControlModel-based Reinforcement Learningreinforcement-learning+1