Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Maximum Entropy Model-based Reinforcement Learning
Recent advances in reinforcement learning have demonstrated its ability to solve hard agent-environment interaction tasks on a super-human level. However, the application of reinforcement learning methods to practical an…
Dota 2modelModel-based Reinforcement Learningreinforcement-learning+2Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization
Estimating the per-state expected cumulative rewards is a critical aspect of reinforcement learning approaches, however the experience is obtained, but standard deep neural-network function-approximation methods are ofte…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Model-Based Reinforcement Learning via Imagination with Derived Memory
Model-based reinforcement learning aims to improve the sample efficiency of policy learning by modeling the dynamics of the environment. Recently, the latent dynamics model is further developed to enable fast planning in…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Weighted model estimation for offline model-based reinforcement learning
This paper discusses model estimation in offline model-based reinforcement learning (MBRL), which is important for subsequent policy improvement using an estimated model. From the viewpoint of covariate shift, a natural …
Density Ratio EstimationmodelModel-based Reinforcement LearningOff-policy evaluation+3Improving gearshift controllers for electric vehicles with reinforcement learning
During a multi-speed transmission development process, the final calibration of the gearshift controller parameters is usually performed on a physical test bench. Engineers typically treat the mapping from the controller…
Experimental DesignModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Learning State Representations via Retracing in Reinforcement Learning
We propose learning via retracing, a novel self-supervised approach for learning the state representation (and the associated dynamics model) for reinforcement learning tasks. In addition to the predictive (reconstructio…
continuous-controlContinuous ControlModel-based Reinforcement Learningreinforcement-learning+3On Effective Scheduling of Model-based Reinforcement Learning
Model-based reinforcement learning has attracted wide attention due to its superior sample efficiency. Despite its impressive success so far, it is still unclear how to appropriately schedule the important hyperparameter…
continuous-controlContinuous ControlModel-based Reinforcement Learningreinforcement-learning+3Approximate infinite-horizon predictive control
Predictive control is frequently used for control problems involving constraints. Being an optimization based technique utilizing a user specified so-called stage cost, performance properties, i.e., bounds on the infinit…
Model-based Reinforcement LearningFree Will Belief as a consequence of Model-based Reinforcement Learning
The debate on whether or not humans have free will has been raging for centuries. Although there are good arguments based on our current understanding of the laws of nature for the view that it is not possible for humans…
Common Sense ReasoningDecision MakingModel-based Reinforcement Learningreinforcement-learning+1Model-Based Reinforcement Learning via Stochastic Hybrid Models
Optimal control of general nonlinear systems is a central challenge in automation. Enabled by powerful function approximators, data-driven approaches to control have recently successfully tackled challenging applications…
Imitation LearningmodelModel-based Reinforcement Learningreinforcement-learning+4Look Before You Leap: Safe Model-Based Reinforcement Learning with Human Intervention
Safety has become one of the main challenges of applying deep reinforcement learning to real world systems. Currently, the incorporation of external knowledge such as human oversight is the only means to prevent the agen…
BlockingDecision MakingDeep Reinforcement LearningModel-based Reinforcement Learning+3Risk Sensitive Model-Based Reinforcement Learning using Uncertainty Guided Planning
Identifying uncertainty and taking mitigating actions is crucial for safe and trustworthy reinforcement learning agents, especially when deployed in high-risk environments. In this paper, risk sensitivity is promoted in …
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Procedural Generalization by Planning with Self-Supervised World Models
One of the key promises of model-based reinforcement learning is the ability to generalize using an internal model of the world to make predictions in novel environments and tasks. However, the generalization ability of …
BenchmarkingMeta-LearningModel-based Reinforcement LearningRepresentation LearningModel based Multi-agent Reinforcement Learning with Tensor Decompositions
A challenge in multi-agent reinforcement learning is to be able to generalize over intractable state-action spaces. Inspired from Tesseract [Mahajan et al., 2021], this position paper investigates generalisation in state…
Model-based Reinforcement LearningMulti-agent Reinforcement LearningPositionreinforcement-learning+2DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical Representations
Top-performing Model-Based Reinforcement Learning (MBRL) agents, such as Dreamer, learn the world model by reconstructing the image observations. Hence, they often fail to discard task-irrelevant details and struggle to …
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Learning Robust Controllers Via Probabilistic Model-Based Policy Search
Model-based Reinforcement Learning estimates the true environment through a world model in order to approximate the optimal policy. This family of algorithms usually benefits from better sample efficiency than their mode…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Operator Shifting for Model-based Policy Evaluation
In model-based reinforcement learning, the transition matrix and reward vector are often estimated from random samples subject to noise. Even if the estimated model is an unbiased estimate of the true underlying model, t…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Multitask Adaptation by Retrospective Exploration with Learned World Models
Model-based reinforcement learning (MBRL) allows solving complex tasks in a sample-efficient manner. However, no information is reused between the tasks. In this work, we propose a meta-learned addressing model called RA…
Model-based Reinforcement LearningVariational Predictive Routing with Nested Subjective Timescales
Discovery and learning of an underlying spatiotemporal hierarchy in sequential data is an important topic for machine learning. Despite this, little work has been done to explore hierarchical generative models that can f…
Event DetectionModel-based Reinforcement LearningModel-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-level
Microservice-based architectures enable different aspects of web applications to be created and updated independently, even after deployment. Associated technologies such as service mesh provide application-level fault r…
AttributeManagementModel-based Reinforcement LearningMulti-agent Reinforcement Learning+1