Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Model-based Policy Search for Partially Measurable Systems
In this paper, we propose a Model-Based Reinforcement Learning (MBRL) algorithm for Partially Measurable Systems (PMS), i.e., systems where the state can not be directly measured, but must be estimated through proper sta…
Gaussian ProcessesmodelModel-based Reinforcement Learningreinforcement-learning+1Model-Based Reinforcement Learning for Approximate Optimal Control with Temporal Logic Specifications
In this paper we study the problem of synthesizing optimal control policies for uncertain continuous-time nonlinear systems from syntactically co-safe linear temporal logic (scLTL) formulas. We formulate this problem as …
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Biomanufacturing Harvest Optimization with Small Data
In biopharmaceutical manufacturing, fermentation processes play a critical role in productivity and profit. A fermentation process uses living cells with complex biological mechanisms, leading to high variability in the …
Decision MakingModel-based Reinforcement LearningTrust, but verify: model-based exploration in sparse reward environments
We propose $\textit{trust-but-verify}$ (TBV) mechanism, a new method which uses model uncertainty estimates to guide exploration. The mechanism augments graph search planning algorithms by the capacity to deal with lear…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Non-Markovian Predictive Coding For Planning In Latent Space
High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments. In order to handle high-dimensional sensory inputs, existing MBRL approaches…
Model-based Reinforcement LearningRepresentation LearningExtracting Strong Policies for Robotics Tasks from zero-order trajectory optimizers
Solving high-dimensional, continuous robotic tasks is a challenging optimization problem. Model-based methods that rely on zero-order optimizers like the cross-entropy method (CEM) have so far shown strong performance an…
Model-based Reinforcement LearningRepresentation Balancing Offline Model-based Reinforcement Learning
One of the main challenges in offline and off-policy reinforcement learning is to cope with the distribution shift that arises from the mismatch between the target policy and the data collection policy. In this paper, we…
modelModel-based Reinforcement LearningOffline RLreinforcement-learning+2On Trade-offs of Image Prediction in Visual Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) methods have shown strong sample efficiency and performance across a variety of tasks, including when faced with high-dimensional visual observations. These methods learn to pred…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning
Accurately predicting the dynamics of robotic systems is crucial for model-based control and reinforcement learning. The most common way to estimate dynamics is by fitting a one-step ahead prediction model and using it t…
Model-based Reinforcement LearningPredictionreinforcement-learningReinforcement Learning+1Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) methods have shown strong sample efficiency and performance across a variety of tasks, including when faced with high-dimensional visual observations. These methods learn to pred…
Model-based Reinforcement LearningReinforcement Learning (RL)Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation
Reinforcement learning (RL) has shown great promise in optimizing long-term user interest in recommender systems. However, existing RL-based recommendation methods need a large number of interactions for each user to lea…
Model-based Reinforcement LearningRecommendation Systemsreinforcement-learningReinforcement Learning+1Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings
Generation of stroke-based non-photorealistic imagery, is an important problem in the computer vision community. As an endeavor in this direction, substantial recent research efforts have been focused on teaching machine…
Deep Reinforcement LearningModel-based Reinforcement LearningObjectPosition+2Nested Mixture of Experts: Cooperative and Competitive Learning of Hybrid Dynamical System
Model-based reinforcement learning (MBRL) algorithms can attain significant sample efficiency but require an appropriate network structure to represent system dynamics. Current approaches include white-box modeling using…
continuous-controlContinuous ControlMixture-of-ExpertsModel-based Reinforcement LearningModel-based Reinforcement Learning for Continuous Control with Posterior Sampling
Balancing exploration and exploitation is crucial in reinforcement learning (RL). In this paper, we study model-based posterior sampling for reinforcement learning (PSRL) in continuous state-action spaces theoretically a…
continuous-controlContinuous ControlEfficient ExplorationGaussian Processes+6Analog Circuit Design with Dyna-Style Reinforcement Learning
In this work, we present a learning based approach to analog circuit design, where the goal is to optimize circuit performance subject to certain design constraints. One of the aspects that makes this problem challenging…
Layout DesignModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1RL-QN: A Reinforcement Learning Framework for Optimal Control of Queueing Systems
With the rapid advance of information technology, network systems have become increasingly complex and hence the underlying system dynamics are often unknown or difficult to characterize. Finding a good network control p…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Critic PI2: Master Continuous Planning via Policy Improvement with Path Integrals and Deep Actor-Critic Reinforcement Learning
Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods from AlphaGo to Muzero have enjoyed huge success in discrete …
continuous-controlContinuous ControlModel-based Reinforcement Learningreinforcement-learning+2Model-based Reinforcement Learning from Signal Temporal Logic Specifications
Techniques based on Reinforcement Learning (RL) are increasingly being used to design control policies for robotic systems. RL fundamentally relies on state-based reward functions to encode desired behavior of the robot …
Autonomous VehiclesModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Model-Based Reinforcement Learning for Eco-Driving Control of Electric Vehicles
The optimal control problem of determining the speed profile of the vehicle for minimizing energy consumption is a challenging problem that necessitates the consideration of various aspects, such as the vehicle energy co…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningOn the role of planning in model-based deep reinforcement learning
Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learning (MBRL) with deep function approximat…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1