Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization
Model-based reinforcement learning algorithms tend to achieve higher sample efficiency than model-free methods. However, due to the inevitable errors of learned models, model-based methods struggle to achieve the same as…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Augmented Random Search for Quadcopter Control: An alternative to Reinforcement Learning
Model-based reinforcement learning strategies are believed to exhibit more significant sample complexity than model-free strategies to control dynamical systems,such as quadcopters.This belief that Model-based strategies…
continuous-controlContinuous ControlModel-based Reinforcement Learningreinforcement-learning+2Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
Reinforcement learning is well suited for optimizing policies of recommender systems. Current solutions mostly focus on model-free approaches, which require frequent interactions with the real environment, and thus are e…
Generative Adversarial NetworkModel-based Reinforcement LearningRecommendation Systemsreinforcement-learning+2Robo-PlaNet: Learning to Poke in a Day
Recently, the Deep Planning Network (PlaNet) approach was introduced as a model-based reinforcement learning method that learns environment dynamics directly from pixel observations. This architecture is useful for learn…
Model-based Reinforcement LearningPositionReinforcement LearningMBCAL: Sample Efficient and Variance Reduced Reinforcement Learning for Recommender Systems
In recommender systems such as news feed stream, it is essential to optimize the long-term utilities in the continuous user-system interaction processes. Previous works have proved the capability of reinforcement learnin…
counterfactualDeep Reinforcement LearningModel-based Reinforcement LearningRecommendation Systems+3Tustin neural networks: a class of recurrent nets for adaptive MPC of mechanical systems
The use of recurrent neural networks to represent the dynamics of unstable systems is difficult due to the need to properly initialize their internal states, which in most of the cases do not have any physical meaning, c…
Model-based Reinforcement LearningReinforcement LearningLearning to Predict Without Looking Ahead: World Models Without Forward Prediction
Much of model-based reinforcement learning involves learning a model of an agent's world, and training an agent to leverage this model to perform a task more efficiently. While these models are demonstrably useful for ag…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Entity Abstraction in Visual Model-Based Reinforcement Learning
This paper tests the hypothesis that modeling a scene in terms of entities and their local interactions, as opposed to modeling the scene globally, provides a significant benefit in generalizing to physical tasks in a co…
modelModel-based Reinforcement LearningObjectObject Discovery+5Asynchronous Methods for Model-Based Reinforcement Learning
Significant progress has been made in the area of model-based reinforcement learning. State-of-the-art algorithms are now able to match the asymptotic performance of model-free methods while being significantly more data…
modelModel-based Reinforcement LearningMuJoCoreinforcement-learning+2Regularizing Model-Based Planning with Energy-Based Models
Model-based reinforcement learning could enable sample-efficient learning by quickly acquiring rich knowledge about the world and using it to improve behaviour without additional data. Learned dynamics models can be dire…
continuous-controlContinuous ControlModel-based Reinforcement LearningReinforcement LearningBuilding HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation
Buildings sector is one of the major consumers of energy in the United States. The buildings HVAC (Heating, Ventilation, and Air Conditioning) systems, whose functionality is to maintain thermal comfort and indoor air qu…
Deep Reinforcement LearningModel-based Reinforcement LearningModel Predictive Controlreinforcement-learning+4Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models
Humans are masters at quickly learning many complex tasks, relying on an approximate understanding of the dynamics of their environments. In much the same way, we would like our learning agents to quickly adapt to new ta…
Model-based Reinforcement LearningReinforcement LearningReinforcement Learning (RL)Robot Manipulation+1Model-Based Reinforcement Learning Exploiting State-Action Equivalence
Leveraging an equivalence property in the state-space of a Markov Decision Process (MDP) has been investigated in several studies. This paper studies equivalence structure in the reinforcement learning (RL) setup, where …
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Model-based Reinforcement Learning for Predictions and Control for Limit Order Books
We build a profitable electronic trading agent with Reinforcement Learning that places buy and sell orders in the stock market. An environment model is built only with historical observational data, and the RL agent lear…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Multi-step Greedy Reinforcement Learning Algorithms
Multi-step greedy policies have been extensively used in model-based reinforcement learning (RL), both when a model of the environment is available (e.g.,~in the game of Go) and when it is learned. In this paper, we expl…
Continuous ControlGame of GoModel-based Reinforcement LearningMuJoCo+3Model Imitation for Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) aims to learn a dynamic model to reduce the number of interactions with real-world environments. However, due to estimation error, rollouts in the learned model, especially those…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1S2VG: Soft Stochastic Value Gradient method
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…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Policy Tree Network
Decision-time planning policies with implicit dynamics models have been shown to work in discrete action spaces with Q learning. However, decision-time planning with implicit dynamics models in continuous action space ha…
Model-based Reinforcement LearningMuJoCoPolicy Gradient MethodsQ-Learning+3Multi-step Greedy Policies in Model-Free Deep Reinforcement Learning
Multi-step greedy policies have been extensively used in model-based Reinforcement Learning (RL) and in the case when a model of the environment is available (e.g., in the game of Go). In this work, we explore the benefi…
continuous-controlContinuous ControlDeep Reinforcement LearningGame of Go+4Improving Exploration of Deep Reinforcement Learning using Planning for Policy Search
Most Deep Reinforcement Learning methods perform local search and therefore are prone to get stuck on non-optimal solutions. Furthermore, in simulation based training, such as domain-randomized simulation training, the a…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1