Interactive Teaching Algorithms for Inverse Reinforcement Learning

Kamalaruban, Parameswaran; Devidze, Rati; Cevher, Volkan; Singla, Adish

doi:10.48550/arxiv.1905.11867

Cited by 4 publications

(5 citation statements)

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“…Similarly, the research work in [65] used students as RL agents to study the foundations of teaching for the purpose of sequential decision making. The curriculum design problem has been studied in various research topics [66][67][68] where the student is used as a learning agent. Rakhsha [69] investigated the issue of policy teaching when the student is used as an RL agent.…”

Section: Rl Techniques For Modeling Studentsmentioning

confidence: 99%

Reinforcement Learning in Education: A Literature Review

Fahad Mon,

Wasfi,

Hayajneh

et al. 2023

Informatics

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The utilization of reinforcement learning (RL) within the field of education holds the potential to bring about a significant shift in the way students approach and engage with learning and how teachers evaluate student progress. The use of RL in education allows for personalized and adaptive learning, where the difficulty level can be adjusted based on a student’s performance. As a result, this could result in heightened levels of motivation and engagement among students. The aim of this article is to investigate the applications and techniques of RL in education and determine its potential impact on enhancing educational outcomes. It compares the various policies induced by RL with baselines and identifies four distinct RL techniques: the Markov decision process, partially observable Markov decision process, deep RL network, and Markov chain, as well as their application in education. The main focus of the article is to identify best practices for incorporating RL into educational settings to achieve effective and rewarding outcomes. To accomplish this, the article thoroughly examines the existing literature on using RL in education and its potential to advance educational technology. This work provides a thorough analysis of the various techniques and applications of RL in education to answer questions related to the effectiveness of RL in education and its future prospects. The findings of this study will provide researchers with a benchmark to compare the usefulness and effectiveness of commonly employed RL algorithms and provide direction for future research in education.

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Section: Rl Techniques For Modeling Studentsmentioning

confidence: 99%

Reinforcement Learning in Education: A Literature Review

Fahad Mon,

Wasfi,

Hayajneh

et al. 2023

Informatics

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“…[91] uses locality-sensitive sampling to scale IMT to largescale problems. Machine teaching is shown useful in reinforcement learning [24,32,63,76], humanin-the-loop learning [9,31,55], crowd sourcing [72,99,100] and cyber security [2,57,96,97,98]. [9,13,21,35,65,107] study machine teaching from a more theoretical point of view.…”

Section: Related Workmentioning

confidence: 99%

Iterative Teaching by Label Synthesis

Liu

Wang

et al. 2021

Preprint

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In this paper, we consider the problem of iterative machine teaching, where a teacher provides examples sequentially based on the current iterative learner. In contrast to previous methods that have to scan over the entire pool and select teaching examples from it in each iteration, we propose a label synthesis teaching framework where the teacher randomly selects input teaching examples (e.g., images) and then synthesizes suitable outputs (e.g., labels) for them. We show that this framework can avoid costly example selection while still provably achieving exponential teachability. We propose multiple novel teaching algorithms in this framework. Finally, we empirically demonstrate the value of our framework.35th Conference on Neural Information Processing Systems (NeurIPS 2021).

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“…Alternately, active learning techniques have been used to reduce the computational complexity of IRL [46,72,252], as well as strategies that make non-I.I.D. assumptions about the informativeness of the demonstrator [45,187]. Finally, outside of an imitation learning framework, goals for the robot are sometimes specified via natural language commands that must be interpreted in the context of the scene [380].…”

Section: Imitation Learningmentioning

confidence: 99%

A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms

Kroemer,

Niekum,

Konidaris

2019

Preprint

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A key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. The last decade has seen substantial growth in research on the problem of robot manipulation, which aims to exploit the increasing availability of affordable robot arms and grippers to create robots capable of directly interacting with the world to achieve their goals. Learning will be central to such autonomous systems, as the real world contains too much variation for a robot to expect to have an accurate model of its environment, the objects in it, or the skills required to manipulate them, in advance. We aim to survey a representative subset of that research which uses machine learning for manipulation. We describe a formalization of the robot manipulation learning problem that synthesizes existing research into a single coherent framework and highlight the many remaining research opportunities and challenges.

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Interactive Teaching Algorithms for Inverse Reinforcement Learning

Cited by 4 publications

References 0 publications

Reinforcement Learning in Education: A Literature Review

Reinforcement Learning in Education: A Literature Review

Iterative Teaching by Label Synthesis

A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms

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