54 research outputs found
Electrocardiogram Recognization Based on Variational AutoEncoder
Subtle distortions on electrocardiogram (ECG) can help doctors to diagnose some serious larvaceous heart sickness on their patients. However, it is difficult to find them manually because of disturbing factors such as baseline wander and high-frequency noise. In this chapter, we propose a method based on variational autoencoder to distinguish these distortions automatically and efficiently. We test our method on three ECG datasets from Physionet by adding some tiny artificial distortions. Comparing with other approaches adopting autoencoders [e.g., contractive autoencoder, denoising autoencoder (DAE)], the results of our experiment show that our method improves the performance of publically available on ECG analysis on the distortions
Addressing Action Oscillations through Learning Policy Inertia
Deep reinforcement learning (DRL) algorithms have been demonstrated to be
effective in a wide range of challenging decision making and control tasks.
However, these methods typically suffer from severe action oscillations in
particular in discrete action setting, which means that agents select different
actions within consecutive steps even though states only slightly differ. This
issue is often neglected since the policy is usually evaluated by its
cumulative rewards only. Action oscillation strongly affects the user
experience and can even cause serious potential security menace especially in
real-world domains with the main concern of safety, such as autonomous driving.
To this end, we introduce Policy Inertia Controller (PIC) which serves as a
generic plug-in framework to off-the-shelf DRL algorithms, to enables adaptive
trade-off between the optimality and smoothness of the learned policy in a
formal way. We propose Nested Policy Iteration as a general training algorithm
for PIC-augmented policy which ensures monotonically non-decreasing updates
under some mild conditions. Further, we derive a practical DRL algorithm,
namely Nested Soft Actor-Critic. Experiments on a collection of autonomous
driving tasks and several Atari games suggest that our approach demonstrates
substantial oscillation reduction in comparison to a range of commonly adopted
baselines with almost no performance degradation.Comment: Accepted paper on AAAI 202
Continuous Multiagent Control using Collective Behavior Entropy for Large-Scale Home Energy Management
With the increasing popularity of electric vehicles, distributed energy
generation and storage facilities in smart grid systems, an efficient
Demand-Side Management (DSM) is urgent for energy savings and peak loads
reduction. Traditional DSM works focusing on optimizing the energy activities
for a single household can not scale up to large-scale home energy management
problems. Multi-agent Deep Reinforcement Learning (MA-DRL) shows a potential
way to solve the problem of scalability, where modern homes interact together
to reduce energy consumers consumption while striking a balance between energy
cost and peak loads reduction. However, it is difficult to solve such an
environment with the non-stationarity, and existing MA-DRL approaches cannot
effectively give incentives for expected group behavior. In this paper, we
propose a collective MA-DRL algorithm with continuous action space to provide
fine-grained control on a large scale microgrid. To mitigate the
non-stationarity of the microgrid environment, a novel predictive model is
proposed to measure the collective market behavior. Besides, a collective
behavior entropy is introduced to reduce the high peak loads incurred by the
collective behaviors of all householders in the smart grid. Empirical results
show that our approach significantly outperforms the state-of-the-art methods
regarding power cost reduction and daily peak loads optimization
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