Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
The growth and form of knowledge networks by kinesthetic curiosity
Throughout life, we might seek a calling, companions, skills, entertainment, truth, self-knowledge, beauty, and edification. The practice of curiosity can be viewed as an extended and open-ended search for valuable infor…
FormModel-based Reinforcement LearningPhilosophyModel-Based Reinforcement Learning with Value-Targeted Regression
This paper studies model-based reinforcement learning (RL) for regret minimization. We focus on finite-horizon episodic RL where the transition model $P$ belongs to a known family of models $\mathcal{P}$, a special case …
modelModel-based Reinforcement Learningregressionreinforcement-learning+2Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
This paper is concerned with the sample efficiency of reinforcement learning, assuming access to a generative model (or simulator). We first consider $\gamma$-discounted infinite-horizon Markov decision processes (MDPs) …
modelModel-based Reinforcement LearningReinforcement Learning (RL)Mutual Information Maximization for Robust Plannable Representations
Extending the capabilities of robotics to real-world complex, unstructured environments requires the need of developing better perception systems while maintaining low sample complexity. When dealing with high-dimensiona…
Model-based Reinforcement LearningModel-Augmented Actor-Critic: Backpropagating through Paths
Current model-based reinforcement learning approaches use the model simply as a learned black-box simulator to augment the data for policy optimization or value function learning. In this paper, we show how to make more …
modelModel-based Reinforcement LearningLifelong Control of Off-grid Microgrid with Model Based Reinforcement Learning
The lifelong control problem of an off-grid microgrid is composed of two tasks, namely estimation of the condition of the microgrid devices and operational planning accounting for the uncertainties by forecasting the fut…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning
Model-based reinforcement learning (RL) enjoys several benefits, such as data-efficiency and planning, by learning a model of the environment's dynamics. However, learning a global model that can generalize across differ…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Fostering Event Compression using Gated Surprise
Our brain receives a dynamically changing stream of sensorimotor data. Yet, we perceive a rather organized world, which we segment into and perceive as events. Computational theories of cognitive science on event-predict…
Model-based Reinforcement LearningDelay-Aware Model-Based Reinforcement Learning for Continuous Control
Action delays degrade the performance of reinforcement learning in many real-world systems. This paper proposes a formal definition of delay-aware Markov Decision Process and proves it can be transformed into standard MD…
continuous-controlContinuous ControlModel-based Reinforcement LearningMuJoCo+3Model-based reinforcement learning for biological sequence design
The ability to design biological structures such as DNA or proteins would have considerable medical and industrial impact. Doing so presents a challenging black-box optimization problem characterized by the large-batch, …
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Dynamics-Aware Unsupervised Skill Discovery
Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment. A good model can potentially enable planning algorithms to generate a large variety of behaviors…
Model-based Reinforcement LearningModel Based Reinforcement Learning for Atari
Model-free reinforcement learning (RL) can be used to learn effective policies for complex tasks, such as Atari games, even from image observations. However, this typically requires very large amounts of interaction -- s…
Atari GamesmodelModel-based Reinforcement Learningreinforcement-learning+3Sample-Efficient Model-based Actor-Critic for an Interactive Dialogue Task
Human-computer interactive systems that rely on machine learning are becoming paramount to the lives of millions of people who use digital assistants on a daily basis. Yet, further advances are limited by the availabilit…
Model-based Reinforcement LearningReinforcement LearningFlexible and Efficient Long-Range Planning Through Curious Exploration
Identifying algorithms that flexibly and efficiently discover temporally-extended multi-phase plans is an essential step for the advancement of robotics and model-based reinforcement learning. The core problem of long-ra…
Deep Reinforcement LearningImitation LearningModel-based Reinforcement LearningMotion Planning+4Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization
Recent works in high-dimensional model-predictive control and model-based reinforcement learning with learned dynamics and reward models have resorted to population-based optimization methods, such as the Cross-Entropy M…
Model-based Reinforcement LearningModel Predictive ControlReinforcement LearningModeling Survival in model-based Reinforcement Learning
Although recent model-free reinforcement learning algorithms have been shown to be capable of mastering complicated decision-making tasks, the sample complexity of these methods has remained a hurdle to utilizing them in…
Decision MakingmodelModel-based Reinforcement Learningreinforcement-learning+2A Game Theoretic Framework for Model Based Reinforcement Learning
Model-based reinforcement learning (MBRL) has recently gained immense interest due to its potential for sample efficiency and ability to incorporate off-policy data. However, designing stable and efficient MBRL algorithm…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Online Constrained Model-based Reinforcement Learning
Applying reinforcement learning to robotic systems poses a number of challenging problems. A key requirement is the ability to handle continuous state and action spaces while remaining within a limited time and resource …
Autonomous RacingGaussian ProcessesmodelModel-based Reinforcement Learning+3Neural Game Engine: Accurate learning of generalizable forward models from pixels
Access to a fast and easily copied forward model of a game is essential for model-based reinforcement learning and for algorithms such as Monte Carlo tree search, and is also beneficial as a source of unlimited experienc…
GPUModel-based Reinforcement LearningOpenAI GymReinforcement LearningLearning to Fly via Deep Model-Based Reinforcement Learning
Learning to control robots without requiring engineered models has been a long-term goal, promising diverse and novel applications. Yet, reinforcement learning has only achieved limited impact on real-time robot control …
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)