Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
TrafficBots: Towards World Models for Autonomous Driving Simulation and Motion Prediction
Data-driven simulation has become a favorable way to train and test autonomous driving algorithms. The idea of replacing the actual environment with a learned simulator has also been explored in model-based reinforcement…
Autonomous DrivingAutonomous VehiclesModel-based Reinforcement Learningmotion predictionApproximating Energy Market Clearing and Bidding With Model-Based Reinforcement Learning
Energy market rules should incentivize market participants to behave in a market and grid conform way. However, they can also provide incentives for undesired and unexpected strategies if the market design is flawed. Mul…
Model-based Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning+1The Virtues of Laziness in Model-based RL: A Unified Objective and Algorithms
We propose a novel approach to addressing two fundamental challenges in Model-based Reinforcement Learning (MBRL): the computational expense of repeatedly finding a good policy in the learned model, and the objective mis…
Computational EfficiencyModel-based Reinforcement LearningModel-Based Uncertainty in Value Functions
We consider the problem of quantifying uncertainty over expected cumulative rewards in model-based reinforcement learning. In particular, we focus on characterizing the variance over values induced by a distribution over…
continuous-controlContinuous ControlDeep Reinforcement Learningmodel+5Understanding the effect of varying amounts of replay per step
Model-based reinforcement learning uses models to plan, where the predictions and policies of an agent can be improved by using more computation without additional data from the environment, thereby improving sample effi…
Model-based Reinforcement LearningEquivariant MuZero
Deep reinforcement learning repeatedly succeeds in closed, well-defined domains such as games (Chess, Go, StarCraft). The next frontier is real-world scenarios, where setups are numerous and varied. For this, agents need…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)+1Non-zero-sum Game Control for Multi-vehicle Driving via Reinforcement Learning
When a vehicle drives on the road, its behaviors will be affected by surrounding vehicles. Prediction and decision should not be considered as two separate stages because all vehicles make decisions interactively. This p…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Predictable MDP Abstraction for Unsupervised Model-Based RL
A key component of model-based reinforcement learning (RL) is a dynamics model that predicts the outcomes of actions. Errors in this predictive model can degrade the performance of model-based controllers, and complex Ma…
modelModel-based Reinforcement LearningReinforcement Learning (RL)Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function
Probabilistic dynamics model ensemble is widely used in existing model-based reinforcement learning methods as it outperforms a single dynamics model in both asymptotic performance and sample efficiency. In this paper, w…
modelModel-based Reinforcement LearningLearning Control from Raw Position Measurements
We propose a Model-Based Reinforcement Learning (MBRL) algorithm named VF-MC-PILCO, specifically designed for application to mechanical systems where velocities cannot be directly measured. This circumstance, if not adeq…
Model-based Reinforcement LearningPositionSTEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning
Directed Exploration is a crucial challenge in reinforcement learning (RL), especially when rewards are sparse. Information-directed sampling (IDS), which optimizes the information ratio, seeks to do so by augmenting reg…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Minimal Value-Equivalent Partial Models for Scalable and Robust Planning in Lifelong Reinforcement Learning
Learning models of the environment from pure interaction is often considered an essential component of building lifelong reinforcement learning agents. However, the common practice in model-based reinforcement learning i…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Intrinsic Motivation in Model-based Reinforcement Learning: A Brief Review
The reinforcement learning research area contains a wide range of methods for solving the problems of intelligent agent control. Despite the progress that has been made, the task of creating a highly autonomous agent is …
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization
With the fast development of quantitative portfolio optimization in financial engineering, lots of AI-based algorithmic trading strategies have demonstrated promising results, among which reinforcement learning begins to…
Algorithmic TradingDecision MakingGaussian ProcessesModel-based Reinforcement Learning+3Plan To Predict: Learning an Uncertainty-Foreseeing Model for Model-Based Reinforcement Learning
In Model-based Reinforcement Learning (MBRL), model learning is critical since an inaccurate model can bias policy learning via generating misleading samples. However, learning an accurate model can be difficult since th…
Decision MakingmodelModel-based Reinforcement LearningSequential Decision MakingHint assisted reinforcement learning: an application in radio astronomy
Model based reinforcement learning has proven to be more sample efficient than model free methods. On the other hand, the construction of a dynamics model in model based reinforcement learning has increased complexity. D…
AstronomyModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Exploration in Model-based Reinforcement Learning with Randomized Reward
Model-based Reinforcement Learning (MBRL) has been widely adapted due to its sample efficiency. However, existing worst-case regret analysis typically requires optimistic planning, which is not realistic in general. In c…
Efficient ExplorationModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Pontryagin Optimal Control via Neural Networks
Solving real-world optimal control problems are challenging tasks, as the complex, high-dimensional system dynamics are usually unrevealed to the decision maker. It is thus hard to find the optimal control actions numeri…
Model-based Reinforcement LearningMuJoCoReinforcement Learning (RL)Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation
We study model-based reinforcement learning (RL) for episodic Markov decision processes (MDP) whose transition probability is parametrized by an unknown transition core with features of state and action. Despite much rec…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Latent Variable Representation for Reinforcement Learning
Deep latent variable models have achieved significant empirical successes in model-based reinforcement learning (RL) due to their expressiveness in modeling complex transition dynamics. On the other hand, it remains uncl…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)