In this work, we address the above limitation by expanding both the number of valid learning strategies for HLS compiler tuning and the metrics used to evaluate.
reinforcement learning methodology
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Abstract. In this paper, we present a reinforce- ment learning approach for mapping nat- ural language instructions to sequences of executable actions.
RL techniques help optimize driving policies by learning from simulated or real-world driving experiences. In healthcare, RL is applied to personalize treatment.
Deep Reinforce- ment Learning (DRL) approaches have recently gained growing interest and shown superior performance over traditional methods. These DRL-based ...
by J Ord · 2012 · Cited by 263 — Learning by doing (or experiential learning) is based on three assumptions, that: 1. people learn best when they are personally involved in the learning ...
In this paper, we propose to combine reinforcement learning with the modeling of human control strategies in order to address the weaknesses inherent in each ...
We present a model for improving story comprehension through inductive generalization and reinforcement learning, based on classified examples. In the process, ...
We provide a concise introduction to basic approaches to reinforcement learning from the machine learning perspective. The focus is on value function and ...
by J Hackathorn · 2011 · Cited by 372 — The current study sought to examine the effectiveness of four teaching techniques (lec- ture, demonstrations, discussions, and in-class activities) in the ...
At the beginning of the course, announce that you'll be assigning short exercises during class and explain why you're doing it (research shows students learn by ...
Abstract—This article proposes online data-based reinforce- ment learning (RL) algorithm for adaptive output consensus.
“Experiential learning is participative— for example, either making or doing,” ex- plains Erin Driver-Linn, associate provost for institutional research and ...
It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the ...
In this work, we propose an active reinforcement learning method capable of collecting trajectories that can augment existing offline data. With extensive ...
Abstract— This paper compares the control strategies of three reinforcement learning algorithms, namely, Q-learning, SARSA, and Double Q-learning for the ...
by R DuFour · 2016 · Cited by 4289 — Learning by Doing provides comprehensive guidance that can help educators overcome the challenges they may face as they turn their schools into professional ...
Methods of Model-Based Safe Reinforcement Learning. Although accurate models are challenging to build and many applications lack models, model-based Deep RL ( ...
Reinforcement Learning (RL) is a machine learning paradigm that enables artificial agents to learn a strategy for behaving in order to maximise ...
In this review, we perform an analysis of several state-of-the-art Deep RL algorithms and divide them into three main categories, according to their anatomy and ...
