Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Improving Hyperparameter Optimization by Planning Ahead
Hyperparameter optimization (HPO) is generally treated as a bi-level optimization problem that involves fitting a (probabilistic) surrogate model to a set of observed hyperparameter responses, e.g. validation loss, and c…
Hyperparameter OptimizationModel-based Reinforcement LearningModel Predictive Controlreinforcement-learning+3Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation
We study the model-based reward-free reinforcement learning with linear function approximation for episodic Markov decision processes (MDPs). In this setting, the agent works in two phases. In the exploration phase, the …
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Safe Reinforcement Learning Using Robust Control Barrier Functions
Reinforcement Learning (RL) has been shown to be effective in many scenarios. However, it typically requires the exploration of a sufficiently large number of state-action pairs, some of which may be unsafe. Consequently…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1Revisiting Design Choices in Offline Model-Based Reinforcement Learning
Offline reinforcement learning enables agents to leverage large pre-collected datasets of environment transitions to learn control policies, circumventing the need for potentially expensive or unsafe online data collecti…
Bayesian OptimizationModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Near-Optimal Reward-Free Exploration for Linear Mixture MDPs with Plug-in Solver
Although model-based reinforcement learning (RL) approaches are considered more sample efficient, existing algorithms are usually relying on sophisticated planning algorithm to couple tightly with the model-learning proc…
Model-based Reinforcement LearningReinforcement Learning (RL)Mismatched No More: Joint Model-Policy Optimization for Model-Based RL
Many model-based reinforcement learning (RL) methods follow a similar template: fit a model to previously observed data, and then use data from that model for RL or planning. However, models that achieve better training …
modelModel-based Reinforcement LearningReinforcement Learning (RL)Imaginary Hindsight Experience Replay: Curious Model-based Learning for Sparse Reward Tasks
Model-based reinforcement learning is a promising learning strategy for practical robotic applications due to its improved data-efficiency versus model-free counterparts. However, current state-of-the-art model-based met…
FetchPush-v1Model-based Reinforcement LearningOpenAI GymCycle-Consistent World Models for Domain Independent Latent Imagination
End-to-end autonomous driving seeks to solve the perception, decision, and control problems in an integrated way, which can be easier to generalize at scale and be more adapting to new scenarios. However, high costs and …
Autonomous DrivingDomain AdaptationModel-based Reinforcement LearningSafety aware model-based reinforcement learning for optimal control of a class of output-feedback nonlinear systems
The ability to learn and execute optimal control policies safely is critical to realization of complex autonomy, especially where task restarts are not available and/or the systems are safety-critical. Safety requirement…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Learning Dynamics Models for Model Predictive Agents
Model-Based Reinforcement Learning involves learning a \textit{dynamics model} from data, and then using this model to optimise behaviour, most often with an online \textit{planner}. Much of the recent research along the…
modelModel-based Reinforcement LearningDecentralized Cross-Entropy Method for Model-Based Reinforcement Learning
Cross-Entropy Method (CEM) is a popular approach to planning in model-based reinforcement learning. It has so far always taken a \textit{centralized} approach where the sampling distribution is updated \textit{centrally}…
continuous-controlContinuous ControlDecision MakingModel-based Reinforcement Learning+4Gradient Information Matters in Policy Optimization by Back-propagating through Model
Model-based reinforcement learning provides an efficient mechanism to find the optimal policy by interacting with the learned environment. In addition to treating the learned environment like a black-box simulator, a mor…
continuous-controlContinuous ControlModel-based Reinforcement LearningFully Decentralized Model-based Policy Optimization with Networked Agents
Model-based RL is an effective approach for reducing sample complexity. However, when it comes to multi-agent setting where the number of agent is large, the model estimation can be problematic due to the exponential inc…
Model-based Reinforcement LearningReinforcement Learning (RL)MOBA: Multi-teacher Model Based Reinforcement Learning
Although reinforcement learning (RL) shines at solving decision-making problems, it not only requires collecting a large amount of environment data but also is time-consuming for training and interaction, making it hard …
Decision MakingKnowledge DistillationmodelModel-based Reinforcement Learning+4Planning in Stochastic Environments with a Learned Model
Model-based reinforcement learning has proven highly successful. However, learning a model in isolation from its use during planning is problematic in complex environments. To date, the most effective techniques have ins…
Board GamesGame of GomodelModel-based Reinforcement Learning+1Model-based Reinforcement Learning with Ensembled Model-value Expansion
Model-based reinforcement learning (MBRL) methods are often more data-efficient and quicker to converge than their model-free counterparts, but typically rely crucially on accurate modeling of the environment dynamics an…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks
In this paper, we study the problem of learning a repertoire of low-level skills from raw images that can be sequenced to complete long-horizon visuomotor tasks. Reinforcement learning (RL) is a promising approach for ac…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Learning-to-defer for sequential medical decision-making under uncertainty
Learning-to-defer is a framework to automatically defer decision-making to a human expert when ML-based decisions are deemed unreliable. Existing learning-to-defer frameworks are not designed for sequential settings. Tha…
Decision MakingDecision Making Under UncertaintyModel-based Reinforcement LearningReinforcement Learning (RL)+1Federated Ensemble Model-based Reinforcement Learning in Edge Computing
Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that enables collaborative training among geographically distributed and heterogeneous devices without gathering their data. Extending…
Autonomous Drivingcontinuous-controlContinuous ControlDecision Making+9Robust Model-based Reinforcement Learning for Autonomous Greenhouse Control
Due to the high efficiency and less weather dependency, autonomous greenhouses provide an ideal solution to meet the increasing demand for fresh food. However, managers are faced with some challenges in finding appropria…
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1