Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity
Model-based reinforcement learning (RL), which finds an optimal policy using an empirical model, has long been recognized as one of the corner stones of RL. It is especially suitable for multi-agent RL (MARL), as it natu…
Model-based Reinforcement LearningReinforcement Learning (RL)Learning Abstract Models for Strategic Exploration and Fast Reward Transfer
Model-based reinforcement learning (RL) is appealing because (i) it enables planning and thus more strategic exploration, and (ii) by decoupling dynamics from rewards, it enables fast transfer to new reward functions. Ho…
Atari GamesModel-based Reinforcement LearningMontezuma's RevengeNavigate+1The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
Deep model-based Reinforcement Learning (RL) has the potential to substantially improve the sample-efficiency of deep RL. While various challenges have long held it back, a number of papers have recently come out reporti…
General Reinforcement LearningModel-based Reinforcement LearningReinforcement Learning (RL)Representation LearningSelective Dyna-style Planning Under Limited Model Capacity
In model-based reinforcement learning, planning with an imperfect model of the environment has the potential to harm learning progress. But even when a model is imperfect, it may still contain information that is useful …
modelModel-based Reinforcement LearningBidirectional Model-based Policy Optimization
Model-based reinforcement learning approaches leverage a forward dynamics model to support planning and decision making, which, however, may fail catastrophically if the model is inaccurate. Although there are several ex…
Decision MakingmodelModel-based Reinforcement LearningReinforcement Learning (RL)Adaptive Discretization for Model-Based Reinforcement Learning
We introduce the technique of adaptive discretization to design an efficient model-based episodic reinforcement learning algorithm in large (potentially continuous) state-action spaces. Our algorithm is based on optimist…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Model-based Reinforcement Learning: A Survey
Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is a important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and p…
Decision MakingmodelModel-based Reinforcement Learningreinforcement-learning+5Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs
We present two elegant solutions for modeling continuous-time dynamics, in a novel model-based reinforcement learning (RL) framework for semi-Markov decision processes (SMDPs), using neural ordinary differential equation…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Control-Aware Representations for Model-based Reinforcement Learning
A major challenge in modern reinforcement learning (RL) is efficient control of dynamical systems from high-dimensional sensory observations. Learning controllable embedding (LCE) is a promising approach that addresses t…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+2Towards Tractable Optimism in Model-Based Reinforcement Learning
The principle of optimism in the face of uncertainty is prevalent throughout sequential decision making problems such as multi-armed bandits and reinforcement learning (RL). To be successful, an optimistic RL algorithm m…
continuous-controlContinuous ControlDecision Makingmodel+6Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes
Continuously learning to solve unseen tasks with limited experience has been extensively pursued in meta-learning and continual learning, but with restricted assumptions such as accessible task distributions, independent…
Continual LearningDecision MakingGaussian ProcessesMeta-Learning+5Active Learning for Nonlinear System Identification with Guarantees
While the identification of nonlinear dynamical systems is a fundamental building block of model-based reinforcement learning and feedback control, its sample complexity is only understood for systems that either have di…
Active LearningModel-based Reinforcement LearningTrajectory PlanningDelta Schema Network in Model-based Reinforcement Learning
This work is devoted to unresolved problems of Artificial General Intelligence - the inefficiency of transfer learning. One of the mechanisms that are used to solve this problem in the area of reinforcement learning is a…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Green Simulation Assisted Reinforcement Learning with Model Risk for Biomanufacturing Learning and Control
Biopharmaceutical manufacturing faces critical challenges, including complexity, high variability, lengthy lead time, and limited historical data and knowledge of the underlying system stochastic process. To address thes…
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Model Embedding Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) has shown its advantages in sample-efficiency over model-free reinforcement learning (MFRL). Despite the impressive results it achieves, it still faces a trade-off between the ea…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning
Model-based reinforcement learning algorithms with probabilistic dynamical models are amongst the most data-efficient learning methods. This is often attributed to their ability to distinguish between epistemic and aleat…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Variational Model-based Policy Optimization
Model-based reinforcement learning (RL) algorithms allow us to combine model-generated data with those collected from interaction with the real system in order to alleviate the data efficiency problem in RL. However, des…
continuous-controlContinuous ControlmodelModel-based Reinforcement Learning+1Maximum Entropy Model Rollouts: Fast Model Based Policy Optimization without Compounding Errors
Model usage is the central challenge of model-based reinforcement learning. Although dynamics model based on deep neural networks provide good generalization for single step prediction, such ability is over exploited whe…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Learning to Plan via Deep Optimistic Value Exploration
Deep exploration requires coordinated long-term planning. We present a model-based reinforcement learning algorithm that guides policy learning through a value function that exhibits optimism in the face of uncertainty. …
BenchmarkingModel-based Reinforcement LearningReinforcement Learning (RL)Tools for Data-driven Modeling of Within-Hand Manipulation with Underactuated Adaptive Hands
Precise in-hand manipulation is an important skill for a robot to perform tasks in human environments. Practical robotic hands must be low-cost, easy to control and capable. 3D-printed underactuated adaptive hands provid…
Model-based Reinforcement LearningReinforcement Learning (RL)