Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies
Emergence and causality are two fundamental concepts for understanding complex systems. They are interconnected. On one hand, emergence refers to the phenomenon where macroscopic properties cannot be solely attributed to…
Model-based Reinforcement LearningRepresentation LearningDynamic Programming-based Approximate Optimal Control for Model-Based Reinforcement Learning
This article proposes an improved trajectory optimization approach for stochastic optimal control of dynamical systems affected by measurement noise by combining optimal control with maximum likelihood techniques to impr…
Model-based Reinforcement Learningreinforcement-learningSample-Efficient Learning to Solve a Real-World Labyrinth Game Using Data-Augmented Model-Based Reinforcement Learning
Motivated by the challenge of achieving rapid learning in physical environments, this paper presents the development and training of a robotic system designed to navigate and solve a labyrinth game using model-based rein…
Model-based Reinforcement LearningNavigatePositionreinforcement-learning+1Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimization
We consider the problem of quantifying uncertainty over expected cumulative rewards in model-based reinforcement learning. In particular, we focus on characterizing the variance over values induced by a distribution over…
Model-based Reinforcement LearningOffline RLScore-Aware Policy-Gradient Methods and Performance Guarantees using Local Lyapunov Conditions: Applications to Product-Form Stochastic Networks and Queueing Systems
In this paper, we introduce a policy-gradient method for model-based reinforcement learning (RL) that exploits a type of stationary distributions commonly obtained from Markov decision processes (MDPs) in stochastic netw…
FormModel-based Reinforcement LearningPolicy Gradient MethodsReinforcement Learning (RL)Regularity as Intrinsic Reward for Free Play
We propose regularity as a novel reward signal for intrinsically-motivated reinforcement learning. Taking inspiration from child development, we postulate that striving for structure and order helps guide exploration tow…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningLanGWM: Language Grounded World Model
Recent advances in deep reinforcement learning have showcased its potential in tackling complex tasks. However, experiments on visual control tasks have revealed that state-of-the-art reinforcement learning models strugg…
Deep Reinforcement LearningmodelModel-based Reinforcement LearningOut-of-Distribution Generalization+3Autonomous Port Navigation With Ranging Sensors Using Model-Based Reinforcement Learning
Autonomous shipping has recently gained much interest in the research community. However, little research focuses on inland - and port navigation, even though this is identified by countries such as Belgium and the Nethe…
Model-based Reinforcement LearningNavigatereinforcement-learningReinforcement LearningReinforcement Learning with Model Predictive Control for Highway Ramp Metering
In the backdrop of an increasingly pressing need for effective urban and highway transportation systems, this work explores the synergy between model-based and learning-based strategies to enhance traffic flow management…
Model-based Reinforcement LearningModel Predictive Controlreinforcement-learningReinforcement Learning+1An introduction to reinforcement learning for neuroscience
Reinforcement learning (RL) has a rich history in neuroscience, from early work on dopamine as a reward prediction error signal (Schultz et al., 1997) to recent work proposing that the brain could implement a form of 'di…
Deep Reinforcement LearningDistributional Reinforcement LearningMeta Reinforcement LearningModel-based Reinforcement Learning+4Reinforcement Twinning: from digital twins to model-based reinforcement learning
Digital twins promise to revolutionize engineering by offering new avenues for optimization, control, and predictive maintenance. We propose a novel framework for simultaneously training the digital twin of an engineerin…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningDreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing
Model-based reinforcement learning (MBRL) has gained much attention for its ability to learn complex behaviors in a sample-efficient way: planning actions by generating imaginary trajectories with predicted rewards. Desp…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningThe Alignment Ceiling: Objective Mismatch in Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique to make large language models (LLMs) more capable in complex settings. RLHF proceeds as collecting human preference data, training a r…
GSM8KMMLUModel-based Reinforcement Learningreinforcement-learningEfficient Exploration in Continuous-time Model-based Reinforcement Learning
Reinforcement learning algorithms typically consider discrete-time dynamics, even though the underlying systems are often continuous in time. In this paper, we introduce a model-based reinforcement learning algorithm tha…
Efficient ExplorationGaussian ProcessesModel-based Reinforcement Learningreinforcement-learning+1Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness
We present a novel framework for generating adversarial benchmarks to evaluate the robustness of image classification models. Our framework allows users to customize the types of distortions to be optimally applied to im…
Benchmarkingimage-classificationImage ClassificationModel-based Reinforcement Learning+1Relational Object-Centric Actor-Critic
The advances in unsupervised object-centric representation learning have significantly improved its application to downstream tasks. Recent works highlight that disentangled object representations can aid policy learning…
Model-based Reinforcement LearningObjectreinforcement-learningReinforcement Learning+1TD-MPC2: Scalable, Robust World Models for Continuous Control
TD-MPC is a model-based reinforcement learning (RL) algorithm that performs local trajectory optimization in the latent space of a learned implicit (decoder-free) world model. In this work, we present TD-MPC2: a series o…
continuous-controlContinuous ControlDecoderModel-based Reinforcement Learning+1Mind the Model, Not the Agent: The Primacy Bias in Model-based RL
The primacy bias in model-free reinforcement learning (MFRL), which refers to the agent's tendency to overfit early data and lose the ability to learn from new data, can significantly decrease the performance of MFRL alg…
continuous-controlContinuous ControlmodelModel-based Reinforcement Learning+3Tree Search in DAG Space with Model-based Reinforcement Learning for Causal Discovery
Identifying causal structure is central to many fields ranging from strategic decision-making to biology and economics. In this work, we propose CD-UCT, a model-based reinforcement learning method for causal discovery ba…
Causal DiscoveryDecision MakingModel-based Reinforcement Learningreinforcement-learning+1Value-Biased Maximum Likelihood Estimation for Model-based Reinforcement Learning in Discounted Linear MDPs
We consider the infinite-horizon linear Markov Decision Processes (MDPs), where the transition probabilities of the dynamic model can be linearly parameterized with the help of a predefined low-dimensional feature mappin…
Model-based Reinforcement Learning