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Oct 21, 2024 · We propose a framework for model-driven goal-oriented development of training simulators for RL. The framework is based on developing goal models.
Model-Driven Design and Generation of Training Simulators for Reinforcement Learning. Tuesday October 29, 2024 3:30pm - 4:00pm EDT. SEI Jordan Auditorium.
Understand and design of sample-efficient RL algorithms! ... Breaking the sample size barrier in model-based reinforcement learning with a generative model.
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The goal of this study is to further explore the promising intersection of MDE with ML (MDE4ML) through a systematic literature review (SLR).
Jul 23, 2024 · Model-based reinforcement learning (MBRL) is an iterative framework for solving tasks in a partially understood environment.
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Jul 19, 2024 · This paper introduces four co-simulation platforms for testing deep reinforcement learning (DRL)-based control solutions in power systems.
Apr 4, 2020 · Most reinforcement learning agents are trained in simulated environments. And the goal is often to maximize performance in this same environment.
Mar 29, 2021 · It is based on obtaining rewards for learning a new task, ie it consists of training models for decision-making without requiring data for conditioning.
This project focuses on how to implement an Artificial Intelligence (AI) -agent in a Tactical. Simulator (Tacsi). Tacsi is a simulator used by Saab AB, one ...
In RL, simulators (generative models) may be used to derive a policy from the generative model underlying the simulator (model-based learning). Agarwal et al. [ ...