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00:59Uncertainty: Overview
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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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00:54Uncertainty: Confidence Intervals
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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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00:59Propagation of Uncertainty from Random Error
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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01:10Propagation of Uncertainty from Systematic Error
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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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01:12Observational Learning
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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01:14Avoidance Learning and Learned Helplessness
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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Reinforcement Learning with Side Information for the Uncertainties.
1Department of A.I. Software Engineering, Seoul Media Institute of Technology, Seoul 07590, Republic of Korea.
Sensors (Basel, Switzerland)
|December 23, 2022
Summary
Sliding mode reinforcement learning (RL) effectively achieves consensus in multi-agent systems. This novel approach outperforms existing methods, demonstrating superior performance in mean square error.
Area of Science:
- Artificial Intelligence
- Control Theory
- Distributed Computing
Background:
- Growing interest in multi-agent system (MAS) consensus.
- Sliding mode control (SMC) offers robust control against uncertainties.
- Previous work integrated SMC with approximate dynamic programming for optimal control.
Purpose of the Study:
- Introduce SMC into a conventional reinforcement learning (RL) framework.
- Develop a novel sliding mode RL approach for MAS consensus.
- Evaluate the performance of the proposed method against state-of-the-art techniques.
Main Methods:
- Modified twin delayed deep deterministic policy gradient (DDPG) algorithm adapted for consensus.
- Development of a sliding mode reinforcement learning (RL) strategy.
- Numerical experiments to assess performance metrics.
Main Results:
- Sliding mode RL demonstrates superior performance compared to existing RL methods.
- The proposed approach achieves lower mean square error (MSE) than model-based methods.
- Effective consensus achieved in the multi-agent system.
Conclusions:
- Sliding mode RL is a promising technique for achieving robust consensus in MAS.
- The developed method offers significant improvements over current state-of-the-art approaches.
- Further research can explore broader applications of SMC in RL for complex systems.