Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Learning with Imperfect Models: When Multi-step Prediction Mitigates Compounding Error
Compounding error, where small prediction mistakes accumulate over time, presents a major challenge in learning-based control. For example, this issue often limits the performance of model-based reinforcement learning an…
Imitation LearningModel-based Reinforcement LearningProbabilistically safe and efficient model-based Reinforcement Learning
This paper proposes tackling safety-critical stochastic Reinforcement Learning (RL) tasks with a samplebased, model-based approach. At the core of the method lies a Model Predictive Control (MPC) scheme that acts as func…
Model-based Reinforcement LearningModel Predictive Controlreinforcement-learningReinforcement Learning+1Perspective-Shifted Neuro-Symbolic World Models: A Framework for Socially-Aware Robot Navigation
Navigating in environments alongside humans requires agents to reason under uncertainty and account for the beliefs and intentions of those around them. Under a sequential decision-making framework, egocentric navigation…
Decision MakingModel-based Reinforcement LearningRobot NavigationSequential Decision Making+1Look Before Leap: Look-Ahead Planning with Uncertainty in Reinforcement Learning
Model-based reinforcement learning (MBRL) has demonstrated superior sample efficiency compared to model-free reinforcement learning (MFRL). However, the presence of inaccurate models can introduce biases during policy le…
Atari GamesModel-based Reinforcement LearningContinual Reinforcement Learning for HVAC Systems Control: Integrating Hypernetworks and Transfer Learning
Buildings with Heating, Ventilation, and Air Conditioning (HVAC) systems play a crucial role in ensuring indoor comfort and efficiency. While traditionally governed by physics-based models, the emergence of big data has …
Continual LearningDeep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learning+3Counterfactual experience augmented off-policy reinforcement learning
Reinforcement learning control algorithms face significant challenges due to out-of-distribution and inefficient exploration problems. While model-based reinforcement learning enhances the agent's reasoning and planning …
counterfactualCounterfactual InferenceDensity EstimationModel-based Reinforcement Learning+2Entropy-regularized Gradient Estimators for Approximate Bayesian Inference
Effective uncertainty quantification is important for training modern predictive models with limited data, enhancing both accuracy and robustness. While Bayesian methods are effective for this purpose, they can be challe…
Bayesian InferenceModel-based Reinforcement LearningUncertainty QuantificationTowards Causal Model-Based Policy Optimization
Real-world decision-making problems are often marked by complex, uncertain dynamics that can shift or break under changing conditions. Traditional Model-Based Reinforcement Learning (MBRL) approaches learn predictive mod…
counterfactualCounterfactual ReasoningmodelModel-based Reinforcement LearningEnhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse
Multi-agent reinforcement learning (MARL) has shown significant potential in traffic signal control (TSC). However, current MARL-based methods often suffer from insufficient generalization due to the fixed traffic patter…
Model-based Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+1InDRiVE: Intrinsic Disagreement based Reinforcement for Vehicle Exploration through Curiosity Driven Generalized World Model
Model-based Reinforcement Learning (MBRL) has emerged as a promising paradigm for autonomous driving, where data efficiency and robustness are critical. Yet, existing solutions often rely on carefully crafted, task speci…
Autonomous DrivingCollision AvoidanceModel-based Reinforcement LearningLearning Transformer-based World Models with Contrastive Predictive Coding
The DreamerV3 algorithm recently obtained remarkable performance across diverse environment domains by learning an accurate world model based on Recurrent Neural Networks (RNNs). Following the success of model-based rein…
Model-based Reinforcement LearningKnowledge Retention for Continual Model-Based Reinforcement Learning
We propose DRAGO, a novel approach for continual model-based reinforcement learning aimed at improving the incremental development of world models across a sequence of tasks that differ in their reward functions but not …
Continual LearningModel-based Reinforcement Learningreinforcement-learningReinforcement LearningWorld Models for Anomaly Detection during Model-Based Reinforcement Learning Inference
Learning-based controllers are often purposefully kept out of real-world applications due to concerns about their safety and reliability. We explore how state-of-the-art world models in Model-Based Reinforcement Learning…
Anomaly DetectionModel-based Reinforcement LearningDifferentiable Information Enhanced Model-Based Reinforcement Learning
Differentiable environments have heralded new possibilities for learning control policies by offering rich differentiable information that facilitates gradient-based methods. In comparison to prevailing model-free reinfo…
Deformable Object ManipulationmodelModel-based Reinforcement Learningreinforcement-learning+1Spiking World Model with Multi-Compartment Neurons for Model-based Reinforcement Learning
Brain-inspired spiking neural networks (SNNs) have garnered significant research attention in algorithm design and perception applications. However, their potential in the decision-making domain, particularly in model-ba…
Deep Reinforcement LearningmodelModel-based Reinforcement LearningMultimodal Dreaming: A Global Workspace Approach to World Model-Based Reinforcement Learning
Humans leverage rich internal models of the world to reason about the future, imagine counterfactuals, and adapt flexibly to new situations. In Reinforcement Learning (RL), world models aim to capture how the environment…
Model-based Reinforcement LearningReinforcement Learning (RL)Accelerating Model-Based Reinforcement Learning with State-Space World Models
Reinforcement learning (RL) is a powerful approach for robot learning. However, model-free RL (MFRL) requires a large number of environment interactions to learn successful control policies. This is due to the noisy RL t…
Model-based Reinforcement LearningReinforcement Learning (RL)State Space ModelsPrivilegedDreamer: Explicit Imagination of Privileged Information for Rapid Adaptation of Learned Policies
Numerous real-world control problems involve dynamics and objectives affected by unobservable hidden pa- rameters, ranging from autonomous driving to robotic manipu- lation, which cause performance degradation during sim…
Autonomous DrivingDomain AdaptationMeta-LearningModel-based Reinforcement Learning+1Enhancing Offline Model-Based RL via Active Model Selection: A Bayesian Optimization Perspective
Offline model-based reinforcement learning (MBRL) serves as a competitive framework that can learn well-performing policies solely from pre-collected data with the help of learned dynamics models. To fully unleash the po…
Bayesian OptimizationmodelModel-based Reinforcement LearningModel Selection+2Towards Empowerment Gain through Causal Structure Learning in Model-Based RL
In Model-Based Reinforcement Learning (MBRL), incorporating causal structures into dynamics models provides agents with a structured understanding of the environments, enabling efficient decision. Empowerment as an intri…
Causal DiscoveryModel-based Reinforcement Learning