Papers Model-based Reinforcement Learning
“Model-based Reinforcement Learning” 태그가 달린 논문 708편 · 필터 해제
Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy
Model-based reinforcement learning (RL) often achieves higher sample efficiency in practice than model-free RL by learning a dynamics model to generate samples for policy learning. Previous works learn a dynamics model t…
continuous-controlContinuous ControlModel-based Reinforcement LearningMuJoCo+1Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equations
Model-based Reinforcement Learning and Control have demonstrated great potential in various sequential decision making problem domains, including in robotics settings. However, real-world robotics systems often present c…
Decision MakingMeta-LearningModel-based Reinforcement Learningreinforcement-learning+2Skill-based Model-based Reinforcement Learning
Model-based reinforcement learning (RL) is a sample-efficient way of learning complex behaviors by leveraging a learned single-step dynamics model to plan actions in imagination. However, planning every action for long-h…
modelModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1Learning to Selectively Learn for Weakly Supervised Paraphrase Generation with Model-based Reinforcement Learning
Paraphrase generation is an important language generation task attempting to interpret user intents and systematically generate new phrases of identical meanings to the given ones. However, the effectiveness of paraphras…
Model-based Reinforcement LearningParaphrase GenerationText GenerationProvably Efficient Reinforcement Learning for Online Adaptive Influence Maximization
Online influence maximization aims to maximize the influence spread of a content in a social network with unknown network model by selecting a few seed nodes. Recent studies followed a non-adaptive setting, where the see…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Masked World Models for Visual Control
Visual model-based reinforcement learning (RL) has the potential to enable sample-efficient robot learning from visual observations. Yet the current approaches typically train a single model end-to-end for learning both …
Model-based Reinforcement LearningReinforcement Learning (RL)Representation LearningCausal Dynamics Learning for Task-Independent State Abstraction
Learning dynamics models accurately is an important goal for Model-Based Reinforcement Learning (MBRL), but most MBRL methods learn a dense dynamics model which is vulnerable to spurious correlations and therefore genera…
Model-based Reinforcement LearningModel-Based Imitation Learning Using Entropy Regularization of Model and Policy
Approaches based on generative adversarial networks for imitation learning are promising because they are sample efficient in terms of expert demonstrations. However, training a generator requires many interactions with …
counterfactualImitation LearningmodelModel-based Reinforcement Learning+3Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars
This paper presents a novel approach that supports natural language voice instructions to guide deep reinforcement learning (DRL) algorithms when training self-driving cars. DRL methods are popular approaches for autonom…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+2Using cognitive psychology to understand GPT-3
We study GPT-3, a recent large language model, using tools from cognitive psychology. More specifically, we assess GPT-3's decision-making, information search, deliberation, and causal reasoning abilities on a battery of…
Decision MakingLanguage ModelingLanguage ModellingLarge Language Model+1Guided Safe Shooting: model based reinforcement learning with safety constraints
In the last decade, reinforcement learning successfully solved complex control tasks and decision-making problems, like the Go board game. Yet, there are few success stories when it comes to deploying those algorithms to…
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1A Survey on Model-based Reinforcement Learning
Reinforcement learning (RL) solves sequential decision-making problems via a trial-and-error process interacting with the environment. While RL achieves outstanding success in playing complex video games that allow huge …
Decision MakingmodelModel-based Reinforcement LearningOffline RL+5A Look at Value-Based Decision-Time vs. Background Planning Methods Across Different Settings
In model-based reinforcement learning (RL), an agent can leverage a learned model to improve its way of behaving in different ways. Two of the prevalent ways to do this are through decision-time and background planning m…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Transfer LearningA Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning
The generalization of model-based reinforcement learning (MBRL) methods to environments with unseen transition dynamics is an important yet challenging problem. Existing methods try to extract environment-specified infor…
Model-based Reinforcement LearningPredictionreinforcement-learningReinforcement Learning (RL)Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning
Reinforcement Learning (RL) methods are typically applied directly in environments to learn policies. In some complex environments with continuous state-action spaces, sparse rewards, and/or long temporal horizons, learn…
D4RLModel-based Reinforcement LearningOffline RLreinforcement-learning+1Model-Based Reinforcement Learning for Offline Zero-Sum Markov Games
This paper makes progress towards learning Nash equilibria in two-player zero-sum Markov games from offline data. Specifically, consider a $\gamma$-discounted infinite-horizon Markov game with $S$ states, where the max-p…
Model-based Reinforcement Learningreinforcement-learningReinforcement Learning (RL)A Model-Based Reinforcement Learning Approach for PID Design
Proportional-integral-derivative (PID) controller is widely used across various industrial process control applications because of its straightforward implementation. However, it can be challenging to fine-tune the PID p…
Model-based Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Adaptive Rollout Length for Model-Based RL Using Model-Free Deep RL
Model-based reinforcement learning promises to learn an optimal policy from fewer interactions with the environment compared to model-free reinforcement learning by learning an intermediate model of the environment in or…
Decision MakingDeep Reinforcement LearningmodelModel-based Reinforcement Learning+4Goal-Space Planning with Subgoal Models
This paper investigates a new approach to model-based reinforcement learning using background planning: mixing (approximate) dynamic programming updates and model-free updates, similar to the Dyna architecture. Backgroun…
Model-based Reinforcement LearningReinforcement Learning (RL)Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning
The quintessential model-based reinforcement-learning agent iteratively refines its estimates or prior beliefs about the true underlying model of the environment. Recent empirical successes in model-based reinforcement l…
Decision MakingModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+2