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Supervised Human-Guided Data Exploration
Peer reviewe
A symbiotic human–machine learning approach for production ramp-up
Constantly shorter product lifecycles and the high number of product variants necessitate frequent production system reconfigurations and changeovers. Shortening ramp-up and changeover times is essential to achieve the agility required to respond to these challenges. This work investigates a symbiotic human–machine environment, which combines a formal framework for capturing structured ramp-up experiences from expert production engineers with a reinforcement learning method to formulate effective ramp-up policies. Such learned policies have been shown to reduce unnecessary iterations in human decision-making processes by suggesting the most appropriate actions for different ramp-up states. One of the key challenges for machine learning based methods, particularly for episodic problems with complex state-spaces, such as ramp-up, is the exploration strategy that can maximize the information gain while minimizing the number of exploration steps required to find good policies. This paper proposes an exploration strategy for reinforcement learning, guided by a human expert. The proposed approach combines human intelligence with machine’s capability for processing data quickly, accurately, and reliably. The efficiency of the proposed human exploration guided machine learning strategy is assessed by comparing it with three machine-based exploration strategies. To test and compare the four strategies, a ramp-up emulator was built, based on system experimentation and user experience. The results of the experiments show that human-guided exploration can achieve close to optimal behavior, with far less data than what is needed for traditional machine-based strategies
Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback
Exploration and reward specification are fundamental and intertwined
challenges for reinforcement learning. Solving sequential decision-making tasks
requiring expansive exploration requires either careful design of reward
functions or the use of novelty-seeking exploration bonuses. Human supervisors
can provide effective guidance in the loop to direct the exploration process,
but prior methods to leverage this guidance require constant synchronous
high-quality human feedback, which is expensive and impractical to obtain. In
this work, we present a technique called Human Guided Exploration (HuGE), which
uses low-quality feedback from non-expert users that may be sporadic,
asynchronous, and noisy. HuGE guides exploration for reinforcement learning not
only in simulation but also in the real world, all without meticulous reward
specification. The key concept involves bifurcating human feedback and policy
learning: human feedback steers exploration, while self-supervised learning
from the exploration data yields unbiased policies. This procedure can leverage
noisy, asynchronous human feedback to learn policies with no hand-crafted
reward design or exploration bonuses. HuGE is able to learn a variety of
challenging multi-stage robotic navigation and manipulation tasks in simulation
using crowdsourced feedback from non-expert users. Moreover, this paradigm can
be scaled to learning directly on real-world robots, using occasional,
asynchronous feedback from human supervisors
Constraining the Size Growth of the Task Space with Socially Guided Intrinsic Motivation using Demonstrations
This paper presents an algorithm for learning a highly redundant inverse
model in continuous and non-preset environments. Our Socially Guided Intrinsic
Motivation by Demonstrations (SGIM-D) algorithm combines the advantages of both
social learning and intrinsic motivation, to specialise in a wide range of
skills, while lessening its dependence on the teacher. SGIM-D is evaluated on a
fishing skill learning experiment.Comment: JCAI Workshop on Agents Learning Interactively from Human Teachers
(ALIHT), Barcelona : Spain (2011
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