Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Intelligent Trainer for Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) has been proposed as a promising alternative solution to tackle the high sampling cost challenge in the canonical reinforcement learning (RL), by leveraging a learned model to ge…
modelModel-based Reinforcement LearningOpenAI Gymreinforcement-learning+2Feedback-Based Tree Search for Reinforcement Learning
Inspired by recent successes of Monte-Carlo tree search (MCTS) in a number of artificial intelligence (AI) application domains, we propose a model-based reinforcement learning (RL) technique that iteratively applies MCTS…
AI AgentModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Generative Temporal Models with Spatial Memory for Partially Observed Environments
In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations during training or via use as part of an exp…
Model-based Reinforcement LearningReinforcement LearningLipschitz Continuity in Model-based Reinforcement Learning
We examine the impact of learning Lipschitz continuous models in the context of model-based reinforcement learning. We provide a novel bound on multi-step prediction error of Lipschitz models where we quantify the error …
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Personalized Dynamics Models for Adaptive Assistive Navigation Systems
Consider an assistive system that guides visually impaired users through speech and haptic feedback to their destination. Existing robotic and ubiquitous navigation technologies (e.g., portable, ground, or wearable syste…
Model-based Reinforcement LearningReinforcement LearningTransfer LearningLearning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situ…
continuous-controlContinuous ControlMeta-LearningMeta Reinforcement Learning+5Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system
Reinforcement learning has emerged as a promising methodology for training robot controllers. However, most results have been limited to simulation due to the need for a large number of samples and the lack of automated-…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)DOP: Deep Optimistic Planning with Approximate Value Function Evaluation
Research on reinforcement learning has demonstrated promising results in manifold applications and domains. Still, efficiently learning effective robot behaviors is very difficult, due to unstructured scenarios, high unc…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement Learning for Planned-Ahead Vision-and-Language Navigation
Existing research studies on vision and language grounding for robot navigation focus on improving model-free deep reinforcement learning (DRL) models in synthetic environments. However, model-free DRL models do not cons…
Deep Reinforcement LearningmodelModel-based Reinforcement Learningreinforcement-learning+5Meta Reinforcement Learning with Latent Variable Gaussian Processes
Learning from small data sets is critical in many practical applications where data collection is time consuming or expensive, e.g., robotics, animal experiments or drug design. Meta learning is one way to increase the d…
Drug DesignGaussian ProcessesMeta-LearningMeta Reinforcement Learning+5Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement Learning
We present an algorithm for rapidly learning controllers for robotics systems. The algorithm follows the model-based reinforcement learning paradigm, and improves upon existing algorithms; namely Probabilistic learning i…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Q-CP: Learning Action Values for Cooperative Planning
Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high unc…
Model-based Reinforcement LearningQ-LearningReinforcement LearningModel-Ensemble Trust-Region Policy Optimization
Model-free reinforcement learning (RL) methods are succeeding in a growing number of tasks, aided by recent advances in deep learning. However, they tend to suffer from high sample complexity, which hinders their use in …
continuous-controlContinuous ControlmodelModel-based Reinforcement Learning+3Learning and Querying Fast Generative Models for Reinforcement Learning
A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models. We show that carefully designed generative models that learn and operate on compact s…
Atari GamesDecision MakingModel-based Reinforcement Learningreinforcement-learning+3Learning the Reward Function for a Misspecified Model
In model-based reinforcement learning it is typical to decouple the problems of learning the dynamics model and learning the reward function. However, when the dynamics model is flawed, it may generate erroneous states t…
modelModel-based Reinforcement LearningReinforcement LearningReinforcement Learning (RL)Learning Dynamic State Abstractions for Model-Based Reinforcement Learning
A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models. We show that carefully designed models that learn predictive and compact state repres…
Atari GamesDecision MakingModel-based Reinforcement Learningreinforcement-learning+3QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds
This paper presents a discrete-time option pricing model that is rooted in Reinforcement Learning (RL), and more specifically in the famous Q-Learning method of RL. We construct a risk-adjusted Markov Decision Process fo…
BenchmarkingModel-based Reinforcement LearningQ-Learningreinforcement-learning+2Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing
Within the context of autonomous driving a model-based reinforcement learning algorithm is proposed for the design of neural network-parameterized controllers. Classical model-based control methods, which include samplin…
Autonomous DrivingDeep Reinforcement LearningModel-based Reinforcement LearningModel Predictive Control+3Learning Approximate Stochastic Transition Models
We examine the problem of learning mappings from state to state, suitable for use in a model-based reinforcement-learning setting, that simultaneously generalize to novel states and can capture stochastic transitions. We…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Fast Model Identification via Physics Engines for Data-Efficient Policy Search
This paper presents a method for identifying mechanical parameters of robots or objects, such as their mass and friction coefficients. Key features are the use of off-the-shelf physics engines and the adaptation of a Bay…
Bayesian OptimizationFrictionModel-based Reinforcement LearningReinforcement Learning