Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous
Collaboration requires agents to align their goals on the fly. Underlying the human ability to align goals with other agents is their ability to predict the intentions of others and actively update their own plans. We pr…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Fast Online Adaptation in Robotics through Meta-Learning Embeddings of Simulated Priors
Meta-learning algorithms can accelerate the model-based reinforcement learning (MBRL) algorithms by finding an initial set of parameters for the dynamical model such that the model can be trained to match the actual dyna…
Meta-LearningModel-based Reinforcement LearningReinforcement LearningPlaNet of the Bayesians: Reconsidering and Improving Deep Planning Network by Incorporating Bayesian Inference
In the present paper, we propose an extension of the Deep Planning Network (PlaNet), also referred to as PlaNet of the Bayesians (PlaNet-Bayes). There has been a growing demand in model predictive control (MPC) in partia…
Bayesian Inferencecontinuous-controlContinuous ControlModel-based Reinforcement Learning+4Policy-Aware Model Learning for Policy Gradient Methods
This paper considers the problem of learning a model in model-based reinforcement learning (MBRL). We examine how the planning module of an MBRL algorithm uses the model, and propose that the model learning module should…
modelModel-based Reinforcement LearningPolicy Gradient MethodsReinforcement LearningModel-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements
In this paper, we propose a derivative-free model learning framework for Reinforcement Learning (RL) algorithms based on Gaussian Process Regression (GPR). In many mechanical systems, only positions can be measured by th…
GPRModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Frequency-based Search-control in Dyna
Model-based reinforcement learning has been empirically demonstrated as a successful strategy to improve sample efficiency. In particular, Dyna is an elegant model-based architecture integrating learning and planning tha…
Model-based Reinforcement LearningReinforcement LearningObjective Mismatch in Model-based Reinforcement Learning
Model-based reinforcement learning (MBRL) has been shown to be a powerful framework for data-efficiently learning control of continuous tasks. Recent work in MBRL has mostly focused on using more advanced function approx…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Ready Policy One: World Building Through Active Learning
Model-Based Reinforcement Learning (MBRL) offers a promising direction for sample efficient learning, often achieving state of the art results for continuous control tasks. However, many existing MBRL methods rely on com…
Active Learningcontinuous-controlContinuous ControlModel-based Reinforcement Learning+1Deep Learning Tubes for Tube MPC
Learning-based control aims to construct models of a system to use for planning or trajectory optimization, e.g. in model-based reinforcement learning. In order to obtain guarantees of safety in this context, uncertainty…
Deep LearningModel-based Reinforcement LearningModel Predictive Controlquantile regression+1Local Policy Optimization for Trajectory-Centric Reinforcement Learning
The goal of this paper is to present a method for simultaneous trajectory and local stabilizing policy optimization to generate local policies for trajectory-centric model-based reinforcement learning (MBRL). This is mot…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling
We address the problem of efficient exploration for transition model learning in the relational model-based reinforcement learning setting without extrinsic goals or rewards. Inspired by human curiosity, we propose goal-…
Decision MakingEfficient ExplorationModel-based Reinforcement Learningreinforcement-learning+1Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping
We present a new model-based reinforcement learning algorithm, Cooperative Prioritized Sweeping, for efficient learning in multi-agent Markov decision processes. The algorithm allows for sample-efficient learning on larg…
Model-based Reinforcement LearningMulti-agent Reinforcement LearningQ-Learningreinforcement-learning+2A Game Theoretic Perspective on Model-Based Reinforcement Learning
We illustrate how game theory is a good framework to understand model-based reinforcement learning (MBRL). We point out that a large class of MBRL algorithms can be viewed as a game between two players: (1) a policy play…
continuous-controlContinuous ControlModel-based Reinforcement Learningreinforcement-learning+2Learning to Combat Compounding-Error in Model-Based Reinforcement Learning
Despite its potential to improve sample complexity versus model-free approaches, model-based reinforcement learning can fail catastrophically if the model is inaccurate. An algorithm should ideally be able to trust an im…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Can Agents Learn by Analogy? An Inferable Model for PAC Reinforcement Learning
Model-based reinforcement learning algorithms make decisions by building and utilizing a model of the environment. However, none of the existing algorithms attempts to infer the dynamics of any state-action pair from kno…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Bayesian Linear Regression on Deep Representations
A simple approach to obtaining uncertainty-aware neural networks for regression is to do Bayesian linear regression (BLR) on the representation from the last hidden layer. Recent work [Riquelme et al., 2018, Azizzadenesh…
Model-based Reinforcement Learningregressionreinforcement-learningReinforcement Learning+1Learning Latent State Spaces for Planning through Reward Prediction
Model-based reinforcement learning methods typically learn models for high-dimensional state spaces by aiming to reconstruct and predict the original observations. However, drawing inspiration from model-free reinforceme…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning
This work tackles the problem of robust zero-shot planning in non-stationary stochastic environments. We study Markov Decision Processes (MDPs) evolving over time and consider Model-Based Reinforcement Learning algorithm…
Model-based Reinforcement LearningReinforcement LearningReinforcement Learning (RL)A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
Reinforcement learning is effective in optimizing policies for recommender systems. Current solutions mostly focus on model-free approaches, which require frequent interactions with a real environment, and thus are expen…
Generative Adversarial NetworkModel-based Reinforcement LearningRecommendation Systemsreinforcement-learning+2Transferable Force-Torque Dynamics Model for Peg-in-hole Task
We present a learning-based force-torque dynamics to achieve model-based control for contact-rich peg-in-hole task using force-only inputs. Learning the force-torque dynamics is challenging because of the ambiguity of th…
Model-based Reinforcement LearningModel Predictive ControlReinforcement Learning