7,147 research outputs found
A deep reinforcement learning based homeostatic system for unmanned position control
Deep Reinforcement Learning (DRL) has been proven to be capable of designing an optimal control theory by minimising the error in dynamic systems. However, in many of the real-world operations, the exact behaviour of the environment is unknown. In such environments, random changes cause the system to reach different states for the same action. Hence, application of DRL for unpredictable environments is difficult as the states of the world cannot be known for non-stationary transition and reward functions. In this paper, a mechanism to encapsulate the randomness of the environment is suggested using a novel bio-inspired homeostatic approach based on a hybrid of Receptor Density Algorithm (an artificial immune system based anomaly detection application) and a Plastic Spiking Neuronal model. DRL is then introduced to run in conjunction with the above hybrid model. The system is tested on a vehicle to autonomously re-position in an unpredictable environment. Our results show that the DRL based process control raised the accuracy of the hybrid model by 32%.N/
Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning
Fine tuning distributed systems is considered to be a craftsmanship, relying
on intuition and experience. This becomes even more challenging when the
systems need to react in near real time, as streaming engines have to do to
maintain pre-agreed service quality metrics. In this article, we present an
automated approach that builds on a combination of supervised and reinforcement
learning methods to recommend the most appropriate lever configurations based
on previous load. With this, streaming engines can be automatically tuned
without requiring a human to determine the right way and proper time to deploy
them. This opens the door to new configurations that are not being applied
today since the complexity of managing these systems has surpassed the
abilities of human experts. We show how reinforcement learning systems can find
substantially better configurations in less time than their human counterparts
and adapt to changing workloads
Characterising the Robustness of Reinforcement Learning for Continuous Control using Disturbance Injection
In this study, we leverage the deliberate and systematic fault-injection
capabilities of an open-source benchmark suite to perform a series of
experiments on state-of-the-art deep and robust reinforcement learning
algorithms. We aim to benchmark robustness in the context of continuous action
spaces -- crucial for deployment in robot control. We find that robustness is
more prominent for action disturbances than it is for disturbances to
observations and dynamics. We also observe that state-of-the-art approaches
that are not explicitly designed to improve robustness perform at a level
comparable to that achieved by those that are. Our study and results are
intended to provide insight into the current state of safe and robust
reinforcement learning and a foundation for the advancement of the field, in
particular, for deployment in robotic systems.Comment: 18 pages, 15 figure
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