Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Control-Oriented Model-Based Reinforcement Learning with Implicit Differentiation
The shortcomings of maximum likelihood estimation in the context of model-based reinforcement learning have been highlighted by an increasing number of papers. When the model class is misspecified or has a limited repres…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Detecting and Adapting to Novelty in Games
Open-world novelty occurs when the rules of an environment can change abruptly, such as when a game player encounters "house rules". To address open-world novelty, game playing agents must be able to detect when novelty …
Knowledge GraphsModel-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning
We present an end-to-end, model-based deep reinforcement learning agent which dynamically attends to relevant parts of its state during planning. The agent uses a bottleneck mechanism over a set-based representation to f…
Deep Reinforcement LearningModel-based Reinforcement LearningOut-of-Distribution Generalizationreinforcement-learning+2Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning
In robotic domains, learning and planning are complicated by continuous state spaces, continuous action spaces, and long task horizons. In this work, we address these challenges with Neuro-Symbolic Relational Transition …
Model-based Reinforcement LearningSafe Model-based Off-policy Reinforcement Learning for Eco-Driving in Connected and Automated Hybrid Electric Vehicles
Connected and Automated Hybrid Electric Vehicles have the potential to reduce fuel consumption and travel time in real-world driving conditions. The eco-driving problem seeks to design optimal speed and power usage profi…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Revisiting Design Choices in Offline Model Based Reinforcement Learning
Offline reinforcement learning enables agents to make use of large pre-collected datasets of environment transitions and learn control policies without the need for potentially expensive or unsafe online data collection.…
Bayesian OptimizationModel-based Reinforcement LearningOffline RLreinforcement-learning+2Learning Modular Robot Control Policies
Modular robots can be rearranged into a new design, perhaps each day, to handle a wide variety of tasks by forming a customized robot for each new task. However, reconfiguring just the mechanism is not sufficient: each d…
Model-based Reinforcement LearningPolicy Optimization in Dynamic Bayesian Network Hybrid Models of Biomanufacturing Processes
Biopharmaceutical manufacturing is a rapidly growing industry with impact in virtually all branches of medicines. Biomanufacturing processes require close monitoring and control, in the presence of complex bioprocess dyn…
Model-based Reinforcement LearningActing upon Imagination: when to trust imagined trajectories in model based reinforcement learning
Model-based reinforcement learning (MBRL) aims to learn model(s) of the environment dynamics that can predict the outcome of its actions. Forward application of the model yields so called imagined trajectories (sequences…
Model-based Reinforcement LearningModel Predictive Controlreinforcement-learningReinforcement Learning (RL)Data-Efficient Reinforcement Learning for Malaria Control
Sequential decision-making under cost-sensitive tasks is prohibitively daunting, especially for the problem that has a significant impact on people's daily lives, such as malaria control, treatment recommendation. The ma…
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+2Learning to drive from a world on rails
We learn an interactive vision-based driving policy from pre-recorded driving logs via a model-based approach. A forward model of the world supervises a driving policy that predicts the outcome of any potential driving t…
Autonomous DrivingCARLA longest6Model-based Reinforcement LearningMBRL-Lib: A Modular Library for Model-based Reinforcement Learning
Model-based reinforcement learning is a compelling framework for data-efficient learning of agents that interact with the world. This family of algorithms has many subcomponents that need to be carefully selected and tun…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Planning with Expectation Models for Control
In model-based reinforcement learning (MBRL), Wan et al. (2019) showed conditions under which the environment model could produce the expectation of the next feature vector rather than the full distribution, or a sample …
Model-based Reinforcement LearningSafe Exploration in Model-based Reinforcement Learning using Control Barrier Functions
This paper develops a model-based reinforcement learning (MBRL) framework for learning online the value function of an infinite-horizon optimal control problem while obeying safety constraints expressed as control barrie…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Safe ExplorationOnline and Offline Reinforcement Learning by Planning with a Learned Model
Learning efficiently from small amounts of data has long been the focus of model-based reinforcement learning, both for the online case when interacting with the environment and the offline case when learning from a fixe…
Atari GamesContinuous ControlModel-based Reinforcement LearningOffline RL+3Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning
Model-based reinforcement learning (RL) is more sample efficient than model-free RL by using imaginary trajectories generated by the learned dynamics model. When the model is inaccurate or biased, imaginary trajectories …
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)GEM: Group Enhanced Model for Learning Dynamical Control Systems
Learning the dynamics of a physical system wherein an autonomous agent operates is an important task. Often these systems present apparent geometric structures. For instance, the trajectories of a robotic manipulator can…
continuous-controlContinuous ControlModel-based Reinforcement LearningReinforcement Learning with a Disentangled Universal Value Function for Item Recommendation
In recent years, there are great interests as well as challenges in applying reinforcement learning (RL) to recommendation systems (RS). In this paper, we summarize three key practical challenges of large-scale RL-based …
Model-based Reinforcement LearningRecommendation Systemsreinforcement-learningReinforcement Learning+1Particle MPC for Uncertain and Learning-Based Control
As robotic systems move from highly structured environments to open worlds, incorporating uncertainty from dynamics learning or state estimation into the control pipeline is essential for robust performance. In this pape…
Model-based Reinforcement LearningModel Predictive ControlState EstimationDEALIO: Data-Efficient Adversarial Learning for Imitation from Observation
In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator…
Imitation LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1