Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Deep Inventory Management
This work provides a Deep Reinforcement Learning approach to solving a periodic review inventory control system with stochastic vendor lead times, lost sales, correlated demand, and price matching. While this dynamic pro…
Deep Reinforcement LearningManagementModel-based Reinforcement Learningreinforcement-learning+2DISCOVER: Deep identification of symbolically concise open-form PDEs via enhanced reinforcement-learning
The working mechanisms of complex natural systems tend to abide by concise and profound partial differential equations (PDEs). Methods that directly mine equations from data are called PDE discovery, which reveals consis…
Deep Reinforcement LearningFormModel-based Reinforcement Learningreinforcement-learning+2Visuo-Tactile Transformers for Manipulation
Learning representations in the joint domain of vision and touch can improve manipulation dexterity, robustness, and sample-complexity by exploiting mutual information and complementary cues. Here, we present Visuo-Tacti…
Model-based Reinforcement LearningRepresentation LearningHyperbolic VAE via Latent Gaussian Distributions
We propose a Gaussian manifold variational auto-encoder (GM-VAE) whose latent space consists of a set of Gaussian distributions. It is known that the set of the univariate Gaussian distributions with the Fisher informati…
Density EstimationModel-based Reinforcement Learningreinforcement-learningReinforcement LearningOptimistic MLE -- A Generic Model-based Algorithm for Partially Observable Sequential Decision Making
This paper introduces a simple efficient learning algorithms for general sequential decision making. The algorithm combines Optimism for exploration with Maximum Likelihood Estimation for model estimation, which is thus …
Decision MakingModel-based Reinforcement LearningReinforcement Learning (RL)Sequential Decision MakingContrastive Unsupervised Learning of World Model with Invariant Causal Features
In this paper we present a world model, which learns causal features using the invariance principle. In particular, we use contrastive unsupervised learning to learn the invariant causal features, which enforces invarian…
Data AugmentationDepth EstimationDepth PredictionModel-based Reinforcement Learning+4Learning Parsimonious Dynamics for Generalization in Reinforcement Learning
Humans are skillful navigators: We aptly maneuver through new places, realize when we are back at a location we have seen before, and can even conceive of shortcuts that go through parts of our environments we have never…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Reinforcement Learning with Non-Exponential Discounting
Commonly in reinforcement learning (RL), rewards are discounted over time using an exponential function to model time preference, thereby bounding the expected long-term reward. In contrast, in economics and psychology, …
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+2FORESEE: Prediction with Expansion-Compression Unscented Transform for Online Policy Optimization
Propagating state distributions through a generic, uncertain nonlinear dynamical model is known to be intractable and usually begets numerical or analytical approximations. We introduce a method for state prediction, cal…
Model-based Reinforcement LearningPerformance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions o…
Federated LearningModel-based Reinforcement LearningQuantizationReinforcement Learning (RL)A Spiking Neural Network Learning Markov Chain
In this paper, the question how spiking neural network (SNN) learns and fixes in its internal structures a model of external world dynamics is explored. This question is important for implementation of the model-based re…
Model-based Reinforcement LearningReinforcement Learning (RL)Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learning
Provably efficient Model-Based Reinforcement Learning (MBRL) based on optimism or posterior sampling (PSRL) is ensured to attain the global optimality asymptotically by introducing the complexity measure of the model. Ho…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Value Summation: A Novel Scoring Function for MPC-based Model-based Reinforcement Learning
This paper proposes a novel scoring function for the planning module of MPC-based reinforcement learning methods to address the inherent bias of using the reward function to score trajectories. The proposed method enhanc…
Model-based Reinforcement LearningMuJoCoReinforcement Learning (RL)Model-based Reinforcement Learning with Multi-step Plan Value Estimation
A promising way to improve the sample efficiency of reinforcement learning is model-based methods, in which many explorations and evaluations can happen in the learned models to save real-world samples. However, when the…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Concept-modulated model-based offline reinforcement learning for rapid generalization
The robustness of any machine learning solution is fundamentally bound by the data it was trained on. One way to generalize beyond the original training is through human-informed augmentation of the original dataset; how…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Variational Inference for Model-Free and Model-Based Reinforcement Learning
Variational inference (VI) is a specific type of approximate Bayesian inference that approximates an intractable posterior distribution with a tractable one. VI casts the inference problem as an optimization problem, mor…
Bayesian InferenceBayesian OptimizationmodelModel-based Reinforcement Learning+5An Analysis of Model-Based Reinforcement Learning From Abstracted Observations
Many methods for Model-based Reinforcement learning (MBRL) in Markov decision processes (MDPs) provide guarantees for both the accuracy of the model they can deliver and the learning efficiency. At the same time, state a…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Model-Based Reinforcement Learning with SINDy
We draw on the latest advancements in the physics community to propose a novel method for discovering the governing non-linear dynamics of physical systems in reinforcement learning (RL). We establish that this method is…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Backward Imitation and Forward Reinforcement Learning via Bi-directional Model Rollouts
Traditional model-based reinforcement learning (RL) methods generate forward rollout traces using the learnt dynamics model to reduce interactions with the real environment. The recent model-based RL method considers the…
Generative Adversarial NetworkModel-based Reinforcement LearningMuJoCoreinforcement-learning+1Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits
We present an adaptive learning Intelligent Tutoring System, which uses model-based reinforcement learning in the form of contextual bandits to assign learning activities to students. The model is trained on the trajecto…
Model-based Reinforcement LearningMulti-Armed Banditsreinforcement-learningReinforcement Learning (RL)