Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
The Value Equivalence Principle for Model-Based Reinforcement Learning
Learning models of the environment from data is often viewed as an essential component to building intelligent reinforcement learning (RL) agents. The common practice is to separate the learning of the model from its use…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Differentiable Physics Models for Real-world Offline Model-based Reinforcement Learning
A limitation of model-based reinforcement learning (MBRL) is the exploitation of errors in the learned models. Black-box models can fit complex dynamics with high fidelity, but their behavior is undefined outside of the …
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Reinforcement Learning with Efficient Active Feature Acquisition
Solving real-life sequential decision making problems under partial observability involves an exploration-exploitation problem. To be successful, an agent needs to efficiently gather valuable information about the state …
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+2Sample-efficient reinforcement learning using deep Gaussian processes
Reinforcement learning provides a framework for learning to control which actions to take towards completing a task through trial-and-error. In many applications observing interactions is costly, necessitating sample-eff…
continuous-controlContinuous ControlGaussian ProcessesModel-based Reinforcement Learning+3Task-Completion Dialogue Policy Learning via Monte Carlo Tree Search with Dueling Network
We introduce a framework of Monte Carlo Tree Search with Double-q Dueling network (MCTS-DDU) for task-completion dialogue policy learning. Different from the previous deep model-based reinforcement learning methods, whic…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Task-Completion Dialogue Policy LearningBayes-Adaptive Deep Model-Based Policy Optimisation
We introduce a Bayesian (deep) model-based reinforcement learning method (RoMBRL) that can capture model uncertainty to achieve sample-efficient policy optimisation. We propose to formulate the model-based policy optimis…
modelModel-based Reinforcement LearningTrajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning
Model-based reinforcement learning (RL) has shown great potential in various control tasks in terms of both sample-efficiency and final performance. However, learning a generalizable dynamics model robust to changes in d…
ClusteringModel-based Reinforcement LearningMultiple-choicePrediction+3Improving the Exploration of Deep Reinforcement Learning in Continuous Domains using Planning for Policy Search
Local policy search is performed by most Deep Reinforcement Learning (D-RL) methods, which increases the risk of getting trapped in a local minimum. Furthermore, the availability of a simulation model is not fully exploi…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Planning with Exploration: Addressing Dynamics Bottleneck in Model-based Reinforcement Learning
Model-based reinforcement learning (MBRL) is believed to have higher sample efficiency compared with model-free reinforcement learning (MFRL). However, MBRL is plagued by dynamics bottleneck dilemma. Dynamics bottleneck …
continuous-controlContinuous ControlDecision MakingModel-based Reinforcement Learning+3Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning
Sample efficiency has been one of the major challenges for deep reinforcement learning. Recently, model-based reinforcement learning has been proposed to address this challenge by performing planning on imaginary traject…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Error Bounds of Imitating Policies and Environments
Imitation learning trains a policy by mimicking expert demonstrations. Various imitation methods were proposed and empirically evaluated, meanwhile, their theoretical understanding needs further studies. In this paper, w…
Imitation LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Safety Verification of Model Based Reinforcement Learning Controllers
Model-based reinforcement learning (RL) has emerged as a promising tool for developing controllers for real world systems (e.g., robotics, autonomous driving, etc.). However, real systems often have constraints imposed o…
Autonomous DrivingmodelModel-based Reinforcement Learningreinforcement-learning+2Model-based Policy Optimization with Unsupervised Model Adaptation
Model-based reinforcement learning methods learn a dynamics model with real data sampled from the environment and leverage it to generate simulated data to derive an agent. However, due to the potential distribution mism…
continuous-controlContinuous ControlmodelModel-based Reinforcement Learning+3Dream and Search to Control: Latent Space Planning for Continuous Control
Learning and planning with latent space dynamics has been shown to be useful for sample efficiency in model-based reinforcement learning (MBRL) for discrete and continuous control tasks. In particular, recent work, for d…
continuous-controlContinuous ControlModel-based Reinforcement LearningReinforcement Learning (RL)Uncertainty-aware Contact-safe Model-based Reinforcement Learning
This letter presents contact-safe Model-based Reinforcement Learning (MBRL) for robot applications that achieves contact-safe behaviors in the learning process. In typical MBRL, we cannot expect the data-driven model to …
Contact-rich ManipulationModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Constrained Model-based Reinforcement Learning with Robust Cross-Entropy Method
This paper studies the constrained/safe reinforcement learning (RL) problem with sparse indicator signals for constraint violations. We propose a model-based approach to enable RL agents to effectively explore the enviro…
Model-based Reinforcement LearningModel Predictive Controlreinforcement-learningReinforcement Learning+2Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control
In this paper we investigate the use of model-based reinforcement learning to assist people with Type 1 Diabetes with insulin dose decisions. The proposed architecture consists of multiple Echo State Networks to predict …
Model-based Reinforcement LearningQ-Learningreinforcement-learningReinforcement Learning+4Reinforcement Learning on Computational Resource Allocation of Cloud-based Wireless Networks
Wireless networks used for Internet of Things (IoT) are expected to largely involve cloud-based computing and processing. Softwarised and centralised signal processing and network switching in the cloud enables flexible …
CPUManagementModel-based Reinforcement Learningreinforcement-learning+2Trust the Model When It Is Confident: Masked Model-based Actor-Critic
It is a popular belief that model-based Reinforcement Learning (RL) is more sample efficient than model-free RL, but in practice, it is not always true due to overweighed model errors. In complex and noisy settings, mode…
continuous-controlContinuous ControlmodelModel-based Reinforcement Learning+1Jointly-Learned State-Action Embedding for Efficient Reinforcement Learning
While reinforcement learning has achieved considerable successes in recent years, state-of-the-art models are often still limited by the size of state and action spaces. Model-free reinforcement learning approaches use s…
Model-based Reinforcement LearningRecommendation Systemsreinforcement-learningReinforcement Learning+1