Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
ToyArchitecture: Unsupervised Learning of Interpretable Models of the World
Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are usually uncomputable, incompatible with the…
Model-based Reinforcement LearningReinforcement LearningRLOC: Neurobiologically Inspired Hierarchical Reinforcement Learning Algorithm for Continuous Control of Nonlinear Dynamical Systems
Nonlinear optimal control problems are often solved with numerical methods that require knowledge of system's dynamics which may be difficult to infer, and that carry a large computational cost associated with iterative …
continuous-controlContinuous ControlHierarchical Reinforcement LearningModel-based Reinforcement Learning+3Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future
In model-based reinforcement learning, the agent interleaves between model learning and planning. These two components are inextricably intertwined. If the model is not able to provide sensible long-term prediction, the …
Imitation LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+3Joint Perception and Control as Inference with an Object-based Implementation
Existing model-based reinforcement learning methods often study perception modeling and decision making separately. We introduce joint Perception and Control as Inference (PCI), a general framework to combine perception …
Bayesian InferenceDecision MakingModel-based Reinforcement LearningObjectModel-Based Reinforcement Learning for Atari
Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction -- s…
Atari GamesAtari Games 100kmodelModel-based Reinforcement Learning+4A General Framework for Structured Learning of Mechanical Systems
Learning accurate dynamics models is necessary for optimal, compliant control of robotic systems. Current approaches to white-box modeling using analytic parameterizations, or black-box modeling using neural networks, ca…
Model-based Reinforcement LearningReinforcement LearningReinforcement Learning (RL)PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
Previously, the exploding gradient problem has been explained to be central in deep learning and model-based reinforcement learning, because it causes numerical issues and instability in optimization. Our experiments in …
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Belief dynamics extraction
Animal behavior is not driven simply by its current observations, but is strongly influenced by internal states. Estimating the structure of these internal states is crucial for understanding the neural basis of behavior…
Model-based Reinforcement LearningReinforcement LearningSuccessor Features Combine Elements of Model-Free and Model-based Reinforcement Learning
A key question in reinforcement learning is how an intelligent agent can generalize knowledge across different inputs. By generalizing across different inputs, information learned for one input can be immediately reused …
Decision MakingmodelModel-based Reinforcement Learningreinforcement-learning+2Multi-Agent Reinforcement Learning with Multi-Step Generative Models
We consider model-based reinforcement learning (MBRL) in 2-agent, high-fidelity continuous control problems -- an important domain for robots interacting with other agents in the same workspace. For non-trivial dynamical…
continuous-controlContinuous ControlDecision MakingModel-based Reinforcement Learning+5Low Level Control of a Quadrotor with Deep Model-Based Reinforcement Learning
Designing effective low-level robot controllers often entail platform-specific implementations that require manual heuristic parameter tuning, significant system knowledge, or long design times. With the rising number of…
GPUModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Optimal Decision-Making in Mixed-Agent Partially Observable Stochastic Environments via Reinforcement Learning
Optimal decision making with limited or no information in stochastic environments where multiple agents interact is a challenging topic in the realm of artificial intelligence. Reinforcement learning (RL) is a popular ap…
Decision MakingImage SegmentationModel-based Reinforcement LearningQ-Learning+4Accelerating Goal-Directed Reinforcement Learning by Model Characterization
We propose a hybrid approach aimed at improving the sample efficiency in goal-directed reinforcement learning. We do this via a two-step mechanism where firstly, we approximate a model from Model-Free reinforcement learn…
modelModel-based Reinforcement LearningQ-Learningreinforcement-learning+2Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems. In this setting, an online user is the environment; neither the reward function nor the environment …
Generative Adversarial NetworkModel-based Reinforcement LearningRecommendation Systemsreinforcement-learning+2VMAV-C: A Deep Attention-based Reinforcement Learning Algorithm for Model-based Control
Recent breakthroughs in Go play and strategic games have witnessed the great potential of reinforcement learning in intelligently scheduling in uncertain environment, but some bottlenecks are also encountered when we gen…
Deep AttentionModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+2Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL
Humans and animals can learn complex predictive models that allow them to accurately and reliably reason about real-world phenomena, and they can adapt such models extremely quickly in the face of unexpected changes. Dee…
Meta-LearningModel-based Reinforcement LearningReinforcement LearningMitigating Planner Overfitting in Model-Based Reinforcement Learning
An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is optimal with respect to its model. Alternati…
modelModel-based Reinforcement LearningPositionreinforcement-learning+2Iterative Value-Aware Model Learning
This paper introduces a model-based reinforcement learning (MBRL) framework that incorporates the underlying decision problem in learning the transition model of the environment. This is in contrast with conventional app…
modelModel-based Reinforcement LearningReinforcement LearningLearning State Representations in Complex Systems with Multimodal Data
Representation learning becomes especially important for complex systems with multimodal data sources such as cameras or sensors. Recent advances in reinforcement learning and optimal control make it possible to design c…
Anomaly DetectionDisentanglementModel-based Reinforcement Learningreinforcement-learning+3A Model-Based Reinforcement Learning Approach for a Rare Disease Diagnostic Task
In this work, we present our various contributions to the objective of building a decision support tool for the diagnosis of rare diseases. Our goal is to achieve a state of knowledge where the uncertainty about the pati…
DiagnosticModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1