Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in environments with complicated dynamics, such as stabilizing the magnetohydrodynamics of a tok…
Deep Reinforcement LearningDictionary LearningModel-based Reinforcement Learningreinforcement-learning+1Mastering Memory Tasks with World Models
Current model-based reinforcement learning (MBRL) agents struggle with long-term dependencies. This limits their ability to effectively solve tasks involving extended time gaps between actions and outcomes, or tasks dema…
Model-based Reinforcement LearningState Space ModelsDr. Strategy: Model-Based Generalist Agents with Strategic Dreaming
Model-based reinforcement learning (MBRL) has been a primary approach to ameliorating the sample efficiency issue as well as to make a generalist agent. However, there has not been much effort toward enhancing the strate…
Model-based Reinforcement LearningGo Beyond Black-box Policies: Rethinking the Design of Learning Agent for Interpretable and Verifiable HVAC Control
Recent research has shown the potential of Model-based Reinforcement Learning (MBRL) to enhance energy efficiency of Heating, Ventilation, and Air Conditioning (HVAC) systems. However, existing methods rely on black-box …
Model-based Reinforcement LearningModel-based deep reinforcement learning for accelerated learning from flow simulations
In recent years, deep reinforcement learning has emerged as a technique to solve closed-loop flow control problems. Employing simulation-based environments in reinforcement learning enables a priori end-to-end optimizati…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement LearningWhen in Doubt, Think Slow: Iterative Reasoning with Latent Imagination
In an unfamiliar setting, a model-based reinforcement learning agent can be limited by the accuracy of its world model. In this work, we present a novel, training-free approach to improving the performance of such agents…
Model-based Reinforcement LearningModel-Based Reinforcement Learning Control of Reaction-Diffusion Problems
Mathematical and computational tools have proven to be reliable in decision-making processes. In recent times, in particular, machine learning-based methods are becoming increasingly popular as advanced support tools. Wh…
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement LearningThe Edge-of-Reach Problem in Offline Model-Based Reinforcement Learning
Offline reinforcement learning aims to train agents from pre-collected datasets. However, this comes with the added challenge of estimating the value of behaviors not covered in the dataset. Model-based methods offer a p…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningTowards Robust Model-Based Reinforcement Learning Against Adversarial Corruption
This study tackles the challenges of adversarial corruption in model-based reinforcement learning (RL), where the transition dynamics can be corrupted by an adversary. Existing studies on corruption-robust RL mostly focu…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)A Distributional Analogue to the Successor Representation
This paper contributes a new approach for distributional reinforcement learning which elucidates a clean separation of transition structure and reward in the learning process. Analogous to how the successor representatio…
Distributional Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement LearningSPO: Sequential Monte Carlo Policy Optimisation
Leveraging planning during learning and decision-making is central to the long-term development of intelligent agents. Recent works have successfully combined tree-based search methods and self-play learning mechanisms t…
Decision MakingModel-based Reinforcement LearningSelf-LearningDifferentially Private Deep Model-Based Reinforcement Learning
We address private deep offline reinforcement learning (RL), where the goal is to train a policy on standard control tasks that is differentially private (DP) with respect to individual trajectories in the dataset. To ac…
continuous-controlContinuous ControlmodelModel-based Reinforcement Learning+3A Multi-step Loss Function for Robust Learning of the Dynamics in Model-based Reinforcement Learning
In model-based reinforcement learning, most algorithms rely on simulating trajectories from one-step models of the dynamics learned on data. A critical challenge of this approach is the compounding of one-step prediction…
Future predictionModel-based Reinforcement Learningreinforcement-learningReinforcement LearningDeep autoregressive density nets vs neural ensembles for model-based offline reinforcement learning
We consider the problem of offline reinforcement learning where only a set of system transitions is made available for policy optimization. Following recent advances in the field, we consider a model-based reinforcement …
D4RLModel-based Reinforcement Learningreinforcement-learningReinforcement LearningControl in Stochastic Environment with Delays: A Model-based Reinforcement Learning Approach
In this paper we are introducing a new reinforcement learning method for control problems in environments with delayed feedback. Specifically, our method employs stochastic planning, versus previous methods that used det…
Atari GamesModel-based Reinforcement Learningreinforcement-learningScheduled Curiosity-Deep Dyna-Q: Efficient Exploration for Dialog Policy Learning
Training task-oriented dialog agents based on reinforcement learning is time-consuming and requires a large number of interactions with real users. How to grasp dialog policy within limited dialog experiences remains an …
Efficient ExplorationModel-based Reinforcement LearningQ-Learningreinforcement-learningAugmenting Replay in World Models for Continual Reinforcement Learning
Continual RL requires an agent to learn new tasks without forgetting previous ones, while improving on both past and future tasks. The most common approaches use model-free algorithms and replay buffers can help to mitig…
Continual LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Locality Sensitive Sparse Encoding for Learning World Models Online
Acquiring an accurate world model online for model-based reinforcement learning (MBRL) is challenging due to data nonstationarity, which typically causes catastrophic forgetting for neural networks (NNs). From the online…
Continual LearningModel-based Reinforcement LearningCoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design
Sampling-based Model Predictive Control (MPC) has been a practical and effective approach in many domains, notably model-based reinforcement learning, thanks to its flexibility and parallelizability. Despite its appealin…
Model-based Reinforcement LearningModel Predictive ControlLaboratory Experiments of Model-based Reinforcement Learning for Adaptive Optics Control
Direct imaging of Earth-like exoplanets is one of the most prominent scientific drivers of the next generation of ground-based telescopes. Typically, Earth-like exoplanets are located at small angular separations from th…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)