Search results for " reinforcement learning"
showing 10 items of 13 documents
A Reinforcement Learning Approach for User Preference-aware Energy Sharing Systems
2021
Energy Sharing Systems (ESS) are envisioned to be the future of power systems. In these systems, consumers equipped with renewable energy generation capabilities are able to participate in an energy market to sell their energy. This paper proposes an ESS that, differently from previous works, takes into account the consumers’ preference, engagement, and bounded rationality. The problem of maximizing the energy exchange while considering such user modeling is formulated and shown to be NP-Hard. To learn the user behavior, two heuristics are proposed: 1) a Reinforcement Learning-based algorithm, which provides a bounded regret and 2) a more computationally efficient heuristic, named BPT- ${K}…
Development of a Simulator for Prototyping Reinforcement Learning-Based Autonomous Cars
2022
Autonomous driving is a research field that has received attention in recent years, with increasing applications of reinforcement learning (RL) algorithms. It is impractical to train an autonomous vehicle thoroughly in the physical space, i.e., the so-called ’real world’; therefore, simulators are used in almost all training of autonomous driving algorithms. There are numerous autonomous driving simulators, very few of which are specifically targeted at RL. RL-based cars are challenging due to the variety of reward functions available. There is a lack of simulators addressing many central RL research tasks within autonomous driving, such as scene understanding, localization and mapping, pla…
Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop
2018
Active inference is an ambitious theory that treats perception, inference and action selection of autonomous agents under the heading of a single principle. It suggests biologically plausible explanations for many cognitive phenomena, including consciousness. In active inference, action selection is driven by an objective function that evaluates possible future actions with respect to current, inferred beliefs about the world. Active inference at its core is independent from extrinsic rewards, resulting in a high level of robustness across e.g.\ different environments or agent morphologies. In the literature, paradigms that share this independence have been summarised under the notion of in…
Designing a multi-layer edge-computing platform for energy-efficient and delay-aware offloading in vehicular networks
2021
Abstract Vehicular networks are expected to support many time-critical services requiring huge amounts of computation resources with very low delay. However, such requirements may not be fully met by vehicle on-board devices due to their limited processing and storage capabilities. The solution provided by 5G is the application of the Multi-Access Edge Computing (MEC) paradigm, which represents a low-latency alternative to remote clouds. Accordingly, we envision a multi-layer job-offloading scheme based on three levels, i.e., the Vehicular Domain, the MEC Domain and Backhaul Network Domain. In such a view, jobs can be offloaded from the Vehicular Domain to the MEC Domain, and even further o…
Robust Adaptive Modulation and Coding (AMC) selection in LTE systems using reinforcement learning
2014
Adaptive Modulation and Coding (AMC) in LTE networks is commonly employed to improve system throughput by ensuring more reliable transmissions. Most of existing AMC methods select the modulation and coding scheme (MCS) using pre-computed mappings between MCS indexes and channel quality indicator (CQI) feedbacks that are periodically sent by the receivers. However, the effectiveness of this approach heavily depends on the assumed channel model. In addition CQI feedback delays may cause throughput losses. In this paper we design a new AMC scheme that exploits a reinforcement learning algorithm to adjust at run-time the MCS selection rules based on the knowledge of the effect of previous AMC d…
Can Interpretable Reinforcement Learning Manage Prosperity Your Way?
2022
Personalisation of products and services is fast becoming the driver of success in banking and commerce. Machine learning holds the promise of gaining a deeper understanding of and tailoring to customers’ needs and preferences. Whereas traditional solutions to financial decision problems frequently rely on model assumptions, reinforcement learning is able to exploit large amounts of data to improve customer modelling and decision-making in complex financial environments with fewer assumptions. Model explainability and interpretability present challenges from a regulatory perspective which demands transparency for acceptance; they also offer the opportunity for improved insight into and unde…
Quantum Machine Learning: A tutorial
2021
This tutorial provides an overview of Quantum Machine Learning (QML), a relatively novel discipline that brings together concepts from Machine Learning (ML), Quantum Computing (QC) and Quantum Information (QI). The great development experienced by QC, partly due to the involvement of giant technological companies as well as the popularity and success of ML have been responsible of making QML one of the main streams for researchers working on fuzzy borders between Physics, Mathematics and Computer Science. A possible, although arguably coarse, classification of QML methods may be based on those approaches that make use of ML in a quantum experimentation environment and those others that take…
Roboception and adaptation in a cognitive robot
2023
In robotics, perception is usually oriented at understanding what is happening in the external world, while few works pay attention to what is occurring in the robot’s body. In this work, we propose an artificial somatosensory system, embedded in a cognitive architecture, that enables a robot to perceive the sensations from its embodiment while executing a task. We called these perceptions roboceptions, and they let the robot act according to its own physical needs in addition to the task demands. Physical information is processed by the robot to behave in a balanced way, determining the most appropriate trade-off between the achievement of the task and its well being. The experiments show …
TERMODINAMICA, CAMPI QUANTICI E FUNZIONI MENTALI
2010
Nel tentativo di fornire spiegazioni esaurienti sul fenomeno della mente umana, il presente saggio scientifico di anatomia comparata e di fisiologia considera alcune tesi di fondo che si collegano a nuovi parametri fisici della realtà che ci circonda.
OMNI-DRL: Learning to Fly in Forests with Omnidirectional Images
2022
Perception is crucial for drone obstacle avoidance in complex, static, and unstructured outdoor environments. However, most navigation solutions based on Deep Reinforcement Learning (DRL) use limited Field-Of-View (FOV) images as input. In this paper, we demonstrate that omnidirectional images improve these methods. Thus, we provide a comparative benchmark of several visual modalities for navigation: ground truth depth, ground truth semantic segmentation, and RGB images. These exhaustive comparisons reveal that it is superior to use an omnidirectional camera to navigate with classical DRL methods. Finally, we show in two different virtual forest environments that adapting the convolution to…