Model-based Reinforcement Learning
195 papers with code • 0 benchmarks • 1 datasets
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Latest papers
CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design
Sampling-based Model Predictive Control (MPC) has been a practical and effective approach in many domains, notably model-based reinforcement learning, thanks to its flexibility and parallelizability.
Laboratory Experiments of Model-based Reinforcement Learning for Adaptive Optics Control
jnousi/po4ao
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RL is an active branch of the machine learning research field, where control of a system is learned through interaction with the environment.
Reinforcement Learning with Model Predictive Control for Highway Ramp Metering
In the backdrop of an increasingly pressing need for effective urban and highway transportation systems, this work explores the synergy between model-based and learning-based strategies to enhance traffic flow management by use of an innovative approach to the problem of highway ramp metering control that embeds Reinforcement Learning techniques within the Model Predictive Control framework.
TD-MPC2: Scalable, Robust World Models for Continuous Control
TD-MPC is a model-based reinforcement learning (RL) algorithm that performs local trajectory optimization in the latent space of a learned implicit (decoder-free) world model.
STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning
weipu-zhang/storm
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NeurIPS 2023
The performance of these algorithms heavily relies on the sequence modeling and generation capabilities of the world model.
A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning
Model-based Reinforcement Learning (MBRL) aims to make agents more sample-efficient, adaptive, and explainable by learning an explicit model of the environment.
Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models
While large language models (LLMs) have demonstrated impressive performance on a range of decision-making tasks, they rely on simple acting processes and fall short of broad deployment as autonomous agents.
Probabilistic Reach-Avoid for Bayesian Neural Networks
matthewwicker/bnnreachavoid
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Such computed lower bounds provide safety certification for the given policy and BNN model.
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
Inspired by human conscious planning, we propose Skipper, a model-based reinforcement learning framework utilizing spatio-temporal abstractions to generalize better in novel situations.
Practical Probabilistic Model-based Deep Reinforcement Learning by Integrating Dropout Uncertainty and Trajectory Sampling
mrjun123/DPETS
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Its loss function is designed to correct the fitting error of neural networks for more accurate prediction of probabilistic models.
