Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play
Model-based algorithms -- algorithms that explore the environment through building and utilizing an estimated model -- are widely used in reinforcement learning practice and theoretically shown to achieve optimal sample …
Model-based Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+1Trust-Region Method with Deep Reinforcement Learning in Analog Design Space Exploration
This paper introduces new perspectives on analog design space search. To minimize the time-to-market, this endeavor better cast as constraint satisfaction problem than global optimization defined in prior arts. We incorp…
Deep Reinforcement Learningglobal-optimizationModel-based Reinforcement Learningreinforcement-learning+1Jointly-Trained State-Action Embedding for Efficient Reinforcement Learning
While reinforcement learning has achieved considerable successes in recent years, state-of-the-art models are often still limited by the size of state and action spaces. Model-free reinforcement learning approaches use s…
Model-based Reinforcement LearningRecommendation Systemsreinforcement-learningReinforcement Learning+1Efficient Exploration for Model-based Reinforcement Learning with Continuous States and Actions
Balancing exploration and exploitation is crucial in reinforcement learning (RL). In this paper, we study the model-based posterior sampling algorithm in continuous state-action spaces theoretically and empirically. Firs…
Efficient ExplorationGaussian ProcessesModel-based Reinforcement LearningModel Predictive Control+2Continual Model-Based Reinforcement Learning with Hypernetworks
Effective planning in model-based reinforcement learning (MBRL) and model-predictive control (MPC) relies on the accuracy of the learned dynamics model. In many instances of MBRL and MPC, this model is assumed to be stat…
Continual LearningmodelModel-based Reinforcement LearningModel Predictive Control+3Dynamic Horizon Value Estimation for Model-based Reinforcement Learning
Existing model-based value expansion methods typically leverage a world model for value estimation with a fixed rollout horizon to assist policy learning. However, the fixed rollout with an inaccurate model has a potenti…
Model-based Reinforcement LearningNovelty Detectionreinforcement-learningReinforcement Learning+1A Contraction Approach to Model-based Reinforcement Learning
Despite its experimental success, Model-based Reinforcement Learning still lacks a complete theoretical understanding. To this end, we analyze the error in the cumulative reward using a contraction approach. We consider …
Imitation LearningmodelModel-based Reinforcement Learningreinforcement-learning+2Reward Maximisation through Discrete Active Inference
Active inference is a probabilistic framework for modelling the behaviour of biological and artificial agents, which derives from the principle of minimising free energy. In recent years, this framework has successfully …
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control
We present a novel approach (DyNODE) that captures the underlying dynamics of a system by incorporating control in a neural ordinary differential equation framework. We conduct a systematic evaluation and comparison of o…
continuous-controlContinuous ControlModel-based Reinforcement Learningreinforcement-learning+2Multi-Objective Model-based Reinforcement Learning for Infectious Disease Control
Severe infectious diseases such as the novel coronavirus (COVID-19) pose a huge threat to public health. Stringent control measures, such as school closures and stay-at-home orders, while having significant effects, also…
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1On the model-based stochastic value gradient for continuous reinforcement learning
For over a decade, model-based reinforcement learning has been seen as a way to leverage control-based domain knowledge to improve the sample-efficiency of reinforcement learning agents. While model-based agents are conc…
Continuous ControlHumanoid ControlModel-based Reinforcement LearningOpenAI Gym+3Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI
Intelligent assistants that follow commands or answer simple questions, such as Siri and Google search, are among the most economically important applications of AI. Future conversational AI assistants promise even great…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
Despite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification and the same data can produce very differe…
Model-based Reinforcement LearningPredictionRecommendation SystemsSample-efficient Cross-Entropy Method for Real-time Planning
Trajectory optimizers for model-based reinforcement learning, such as the Cross-Entropy Method (CEM), can yield compelling results even in high-dimensional control tasks and sparse-reward environments. However, their sam…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey
Deep reinforcement learning has shown remarkable success in the past few years. Highly complex sequential decision making problems have been solved in tasks such as game playing and robotics. Unfortunately, the sample co…
Decision MakingDeep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learning+5Learning Transition Models with Time-delayed Causal Relations
This paper introduces an algorithm for discovering implicit and delayed causal relations between events observed by a robot at arbitrary times, with the objective of improving data-efficiency and interpretability of mode…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction
In the present paper, we propose a decoder-free extension of Dreamer, a leading model-based reinforcement learning (MBRL) method from pixels. Dreamer is a sample- and cost-efficient solution to robot learning, as it is u…
Contrastive LearningData AugmentationDecoderModel-based Reinforcement Learning+4Safe Model-Based Reinforcement Learning for Systems with Parametric Uncertainties
Reinforcement learning has been established over the past decade as an effective tool to find optimal control policies for dynamical systems, with recent focus on approaches that guarantee safety during the learning and/…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Reinforcement Learning with Fast Stabilization in Linear Dynamical Systems
In this work, we study model-based reinforcement learning (RL) in unknown stabilizable linear dynamical systems. When learning a dynamical system, one needs to stabilize the unknown dynamics in order to avoid system blow…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Understanding and Mitigating the Limitations of Prioritized Experience Replay
Prioritized Experience Replay (ER) has been empirically shown to improve sample efficiency across many domains and attracted great attention; however, there is little theoretical understanding of why such prioritized sam…
Autonomous Drivingcontinuous-controlContinuous ControlModel-based Reinforcement Learning