Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Model-Based Reinforcement Learning for Sepsis Treatment
Sepsis is a dangerous condition that is a leading cause of patient mortality. Treating sepsis is highly challenging, because individual patients respond very differently to medical interventions and there is no universal…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
We study the sample complexity of model-based reinforcement learning (henceforth RL) in general contextual decision processes that require strategic exploration to find a near-optimal policy. We design new algorithms for…
modelModel-based Reinforcement LearningReinforcement LearningPolicy Optimization with Model-based Explorations
Model-free reinforcement learning methods such as the Proximal Policy Optimization algorithm (PPO) have successfully applied in complex decision-making problems such as Atari games. However, these methods suffer from hig…
Atari GamesDecision MakingmodelModel-based Reinforcement Learning+3Towards a Simple Approach to Multi-step Model-based Reinforcement Learning
When environmental interaction is expensive, model-based reinforcement learning offers a solution by planning ahead and avoiding costly mistakes. Model-based agents typically learn a single-step transition model. In this…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Incremental learning abstract discrete planning domains and mappings to continuous perceptions
Most of the works on planning and learning, e.g., planning by (model based) reinforcement learning, are based on two main assumptions: (i) the set of states of the planning domain is fixed; (ii) the mapping between the o…
Incremental LearningModel-based Reinforcement LearningReinforcement LearningUnsupervised Exploration with Deep Model-Based Reinforcement Learning
Reinforcement learning (RL) often requires large numbers of trials to solve a single specific task. This is in sharp contrast to human and animal learning: humans and animals can use past experience to acquire an underst…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Understanding the Asymptotic Performance of Model-Based RL Methods
In complex simulated environments, model-based reinforcement learning methods typically lag the asymptotic performance of model-free approaches. This paper uses two MuJoCo environments to understand this gap through a se…
Model-based Reinforcement LearningMuJoCoLearning powerful policies and better dynamics models by encouraging consistency
Model-based reinforcement learning approaches have the promise of being sample efficient. Much of the progress in learning dynamics models in RL has been made by learning models via supervised learning. There is enough e…
Model-based Reinforcement LearningModel-Based Reinforcement Learning via Meta-Policy Optimization
Model-based reinforcement learning approaches carry the promise of being data efficient. However, due to challenges in learning dynamics models that sufficiently match the real-world dynamics, they struggle to achieve th…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning
Model-based reinforcement learning (RL) has proven to be a data efficient approach for learning control tasks but is difficult to utilize in domains with complex observations such as images. In this paper, we present a m…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Learning Stabilizable Dynamical Systems via Control Contraction Metrics
We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics. The key idea is to develop a new control-theoretic regularizer for dynamics fitting rooted in t…
continuous-controlContinuous ControlModel-based Reinforcement LearningReinforcement LearningThe Bottleneck Simulator: A Model-based Deep Reinforcement Learning Approach
Deep reinforcement learning has recently shown many impressive successes. However, one major obstacle towards applying such methods to real-world problems is their lack of data-efficiency. To this end, we propose the Bot…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1A Hierarchical Bayesian Linear Regression Model with Local Features for Stochastic Dynamics Approximation
One of the challenges in model-based control of stochastic dynamical systems is that the state transition dynamics are involved, and it is not easy or efficient to make good-quality predictions of the states. Moreover, t…
Model-based Reinforcement LearningregressionReinforcement LearningAlgorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees
Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL. However, the theoretical understanding of such methods has been rather limited.…
continuous-controlContinuous ControlDeep Reinforcement LearningModel-based Reinforcement Learning+4Accurate Uncertainties for Deep Learning Using Calibrated Regression
Methods for reasoning under uncertainty are a key building block of accurate and reliable machine learning systems. Bayesian methods provide a general framework to quantify uncertainty. However, because of model misspeci…
Deep LearningModel-based Reinforcement LearningregressionReinforcement Learning+4The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces
Dyna is a fundamental approach to model-based reinforcement learning (MBRL) that interleaves planning, acting, and learning in an online setting. In the most typical application of Dyna, the dynamics model is used to gen…
Atari GamesModel-based Reinforcement LearningReinforcement LearningEquivalence Between Wasserstein and Value-Aware Loss for Model-based Reinforcement Learning
Learning a generative model is a key component of model-based reinforcement learning. Though learning a good model in the tabular setting is a simple task, learning a useful model in the approximate setting is challengin…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning
Humans learn to play video games significantly faster than the state-of-the-art reinforcement learning (RL) algorithms. People seem to build simple models that are easy to learn to support planning and strategic explorat…
Model-based Reinforcement LearningObjectreinforcement-learningReinforcement Learning+1Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance. This is especially true with high-capacit…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Object-Oriented Dynamics Predictor
Generalization has been one of the major challenges for learning dynamics models in model-based reinforcement learning. However, previous work on action-conditioned dynamics prediction focuses on learning the pixel-level…
Model-based Reinforcement LearningObjectReinforcement Learning