Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Plug and Play, Model-Based Reinforcement Learning
Sample-efficient generalisation of reinforcement learning approaches have always been a challenge, especially, for complex scenes with many components. In this work, we introduce Plug and Play Markov Decision Processes, …
modelModel-based Reinforcement LearningObjectreinforcement-learning+3Fractional Transfer Learning for Deep Model-Based Reinforcement Learning
Reinforcement learning (RL) is well known for requiring large amounts of data in order for RL agents to learn to perform complex tasks. Recent progress in model-based RL allows agents to be much more data-efficient, as i…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1MBDP: A Model-based Approach to Achieve both Robustness and Sample Efficiency via Double Dropout Planning
Model-based reinforcement learning is a widely accepted solution for solving excessive sample demands. However, the predictions of the dynamics models are often not accurate enough, and the resulting bias may incur catas…
Model-based Reinforcement LearningPhysics-informed Dyna-Style Model-Based Deep Reinforcement Learning for Dynamic Control
Model-based reinforcement learning (MBRL) is believed to have much higher sample efficiency compared to model-free algorithms by learning a predictive model of the environment. However, the performance of MBRL highly rel…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?
We contribute to micro-data model-based reinforcement learning (MBRL) by rigorously comparing popular generative models using a fixed (random shooting) control agent. We find that on an environment that requires multimod…
AcrobotmodelModel-based Reinforcement LearningKnow Thyself: Transferable Visual Control Policies Through Robot-Awareness
Training visual control policies from scratch on a new robot typically requires generating large amounts of robot-specific data. How might we leverage data previously collected on another robot to reduce or even complete…
Model-based Reinforcement LearningTransfer LearningVideo PredictionHigh-Accuracy Model-Based Reinforcement Learning, a Survey
Deep reinforcement learning has shown remarkable success in the past few years. Highly complex sequential decision making problems from game playing and robotics have been solved with deep model-free methods. Unfortunate…
Decision MakingDeep Reinforcement LearningmodelModel-based Reinforcement Learning+7PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration
Model-based Reinforcement Learning (RL) is a popular learning paradigm due to its potential sample efficiency compared to model-free RL. However, existing empirical model-based RL approaches lack the ability to explore. …
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning
Learning in multi-agent systems is highly challenging due to several factors including the non-stationarity introduced by agents' interactions and the combinatorial nature of their state and action spaces. In particular,…
Gaussian ProcessesModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Systematic Evaluation of Causal Discovery in Visual Model Based Reinforcement Learning
Inducing causal relationships from observations is a classic problem in machine learning. Most work in causality starts from the premise that the causal variables themselves are observed. However, for AI agents such as r…
BenchmarkingCausal DiscoveryModel-based Reinforcement Learningreinforcement-learning+2Learning 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)Causal Reinforcement Learning using Observational and Interventional Data
Learning efficiently a causal model of the environment is a key challenge of model-based RL agents operating in POMDPs. We consider here a scenario where the learning agent has the ability to collect online experiences t…
Causal InferenceModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Model-Advantage and Value-Aware Models for Model-Based Reinforcement Learning: Bridging the Gap in Theory and Practice
This work shows that value-aware model learning, known for its numerous theoretical benefits, is also practically viable for solving challenging continuous control tasks in prevalent model-based reinforcement learning al…
continuous-controlContinuous ControlmodelModel-based Reinforcement LearningPredictive Control Using Learned State Space Models via Rolling Horizon Evolution
A large part of the interest in model-based reinforcement learning derives from the potential utility to acquire a forward model capable of strategic long term decision making. Assuming that an agent succeeds in learning…
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)+3Model-Based Reinforcement Learning via Latent-Space Collocation
The ability to plan into the future while utilizing only raw high-dimensional observations, such as images, can provide autonomous agents with broad capabilities. Visual model-based reinforcement learning (RL) methods th…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+2Uncertainty-Aware Model-Based Reinforcement Learning with Application to Autonomous Driving
To further improve the learning efficiency and performance of reinforcement learning (RL), in this paper we propose a novel uncertainty-aware model-based RL (UA-MBRL) framework, and then implement and validate it in auto…
Autonomous DrivingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Lifted Model Checking for Relational MDPs
Probabilistic model checking has been developed for verifying systems that have stochastic and nondeterministic behavior. Given a probabilistic system, a probabilistic model checker takes a property and checks whether or…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Proper Value Equivalence
One of the main challenges in model-based reinforcement learning (RL) is to decide which aspects of the environment should be modeled. The value-equivalence (VE) principle proposes a simple answer to this question: a mod…
Model-based Reinforcement LearningReinforcement Learning (RL)Temporal Predictive Coding For Model-Based Planning In Latent Space
High-dimensional observations are a major challenge in the application of model-based reinforcement learning (MBRL) to real-world environments. To handle high-dimensional sensory inputs, existing approaches use represent…
Model-based Reinforcement LearningRepresentation LearningHyperspace Neighbor Penetration Approach to Dynamic Programming for Model-Based Reinforcement Learning Problems with Slowly Changing Variables in A Continuous State Space
Slowly changing variables in a continuous state space constitute an important category of reinforcement learning and see its application in many domains, such as modeling a climate control system where temperature, humid…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)