Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlow
In the past decade, model-free reinforcement learning (RL) has provided solutions to challenging domains such as robotics. Model-based RL shows the prospect of being more sample-efficient than model-free methods in terms…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning
Substantial advancements to model-based reinforcement learning algorithms have been impeded by the model-bias induced by the collected data, which generally hurts performance. Meanwhile, their inherent sample efficiency …
Deep Reinforcement LearningModel-based Reinforcement LearningModel Predictive Controlreinforcement-learning+2Discriminator Augmented Model-Based Reinforcement Learning
By planning through a learned dynamics model, model-based reinforcement learning (MBRL) offers the prospect of good performance with little environment interaction. However, it is common in practice for the learned model…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning
In real-world tasks, reinforcement learning (RL) agents frequently encounter situations that are not present during training time. To ensure reliable performance, the RL agents need to exhibit robustness against worst-ca…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence
Decision and control are core functionalities of high-level automated vehicles. Current mainstream methods, such as functionality decomposition and end-to-end reinforcement learning (RL), either suffer high time complexi…
Autonomous DrivingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Adapting User Interfaces with Model-based Reinforcement Learning
Adapting an interface requires taking into account both the positive and negative effects that changes may have on the user. A carelessly picked adaptation may impose high costs to the user -- for example, due to surpris…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Solipsistic Reinforcement Learning
We introduce a new model-based reinforcement learning framework that aims to tackle environments with high dimensional state spaces. In contrast to existing approaches, agents under our framework learn a low dimensional …
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Learning State Representations via Temporal Cycle-Consistency Constraint in Model-Based Reinforcement Learning
Representation learning is a popular approach for reinforcement learning (RL) tasks with partially observable Markov decision processes. Existing works on learning representations utilise the dynamics models in model-bas…
continuous-controlContinuous ControlModel-based Reinforcement Learningreinforcement-learning+2Less Suboptimal Learning and Control in Variational POMDPs
A recently uncovered pitfall in learning generative models with amortised variational inference, the conditioning gap, questions common practices in model-based reinforcement learning. Withholding a part of the quantitie…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Learning Task Informed Abstractions
Current model-based reinforcement learning methods struggle when operating from complex visual scenes due to their inability to prioritize task-relevant features. To mitigate this problem, we propose learning Task Inform…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Minimax Model Learning
We present a novel off-policy loss function for learning a transition model in model-based reinforcement learning. Notably, our loss is derived from the off-policy policy evaluation objective with an emphasis on correcti…
modelModel-based Reinforcement LearningOff-policy evaluationreinforcement-learning+1On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
Model-based Reinforcement Learning (MBRL) is a promising framework for learning control in a data-efficient manner. MBRL algorithms can be fairly complex due to the separate dynamics modeling and the subsequent planning …
Hyperparameter OptimizationModel-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Visualizing MuZero Models
MuZero, a model-based reinforcement learning algorithm that uses a value equivalent dynamics model, achieved state-of-the-art performance in Chess, Shogi and the game of Go. In contrast to standard forward dynamics model…
Game of GoModel-based Reinforcement LearningCombining Off and On-Policy Training in Model-Based Reinforcement Learning
The combination of deep learning and Monte Carlo Tree Search (MCTS) has shown to be effective in various domains, such as board and video games. AlphaGo represented a significant step forward in our ability to learn comp…
Atari GamesBoard GamesModel-based Reinforcement Learningreinforcement-learning+1SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning
In this paper, we consider risk-sensitive sequential decision-making in Reinforcement Learning (RL). Our contributions are two-fold. First, we introduce a novel and coherent quantification of risk, namely composite risk,…
Decision MakingDistributional Reinforcement LearningModel-based Reinforcement Learningreinforcement-learning+3Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach
Reliable automatic evaluation of dialogue systems under an interactive environment has long been overdue. An ideal environment for evaluating dialog systems, also known as the Turing test, needs to involve human interact…
Model-based Reinforcement LearningOff-policy evaluationreinforcement-learningReinforcement Learning+2Model-Invariant State Abstractions for Model-Based Reinforcement Learning
Accuracy and generalization of dynamics models is key to the success of model-based reinforcement learning (MBRL). As the complexity of tasks increases, so does the sample inefficiency of learning accurate dynamics model…
continuous-controlContinuous ControlmodelModel-based Reinforcement Learning+4Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision
Using a model of the environment, reinforcement learning agents can plan their future moves and achieve superhuman performance in board games like Chess, Shogi, and Go, while remaining relatively sample-efficient. As dem…
Board GamesModel-based Reinforcement LearningOpenAI Gymreinforcement-learning+1Continuous-Time Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) approaches rely on discrete-time state transition models whereas physical systems and the vast majority of control tasks operate in continuous-time. To avoid time-discretization …
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application
In this paper, we present a Model-Based Reinforcement Learning (MBRL) algorithm named \emph{Monte Carlo Probabilistic Inference for Learning COntrol} (MC-PILCO). The algorithm relies on Gaussian Processes (GPs) to model …
Gaussian ProcessesModel-based Reinforcement Learning