Assistive Robotics is a class of robotics concerned with aiding humans in daily care tasks that they may be inhibited from doing due to disabilities or age. While research has demonstrated that classical control methods can be used to design policies to complete these tasks, these methods can be difficult to generalize to a variety of instantiations of a task. Reinforcement learning can provide a solution to this issue, wherein robots are trained in simulation and their policies are transferred to real-world machines. In this work, we replicate a published baseline for training robots on three tasks in the Assistive Gym environment, and we explore the usage of a Recurrent Neural Network and Phasic Policy Gradient learning to augment the original work. Our baseline implementation meets or exceeds the baseline of the original work, however, we found that our explorations into the new methods was not as effective as we anticipated. We discuss the results of our baseline and some thoughts on why our new methods were not as successful.

We propose a new model-based offline RL framework, called Adversarial Models for Offline Reinforcement Learning (ARMOR), which can robustly learn policies to improve upon an arbitrary baseline policy regardless of data coverage. Based on the concept of relative pessimism, ARMOR is designed to optimize for the worst-case relative performance when facing uncertainty. In theory, we prove that the learned policy of ARMOR never degrades the performance of the baseline policy with any admissible hyperparameter, and can learn to compete with the best policy within data coverage when the hyperparameter is well tuned, and the baseline policy is supported by the data. Such a robust policy improvement property makes ARMOR especially suitable for building real-world learning systems, because in practice ensuring no performance degradation is imperative before considering any benefit learning can bring.

With a handful of demonstration examples, large-scale language models show strong capability to perform various tasks by in-context learning from these examples, without any fine-tuning. We demonstrate that in-context learning performance can be highly unstable across samples of examples, indicating the idiosyncrasies of how language models acquire information. We formulate example selection for in-context learning as a sequential decision problem, and propose a reinforcement learning algorithm for identifying generalizable policies to select demonstration examples. For GPT-2, our learned policies demonstrate strong abilities of generalizing to unseen tasks in training, with a $5.8\%$ improvement on average. Examples selected from our learned policies can even achieve a small improvement on GPT-3 Ada. However, the improvement diminishes on larger GPT-3 models, suggesting emerging capabilities of large language models.

Abstract: This paper presents a deep reinforcement learning (DRL) solution for power control in wireless communications, describes its embedded implementation with WiFi transceivers for a WiFi network system, and evaluates the performance with high-fidelity emulation tests. In a multi-hop wireless network, each mobile node measures its link quality and signal strength, and controls its transmit power. As a model-free solution, reinforcement learning allows nodes to adapt their actions by observing the states and maximize their cumulative rewards over time. For each node, the state consists of transmit power, link quality and signal strength; the action adjusts the transmit power; and the reward combines energy efficiency (throughput normalized by energy consumption) and penalty of changing the transmit power. As the state space is large, Q-learning is hard to implement on embedded platforms with limited memory and processing power.

The Cognitive Software leverages automation, big data scalability, speed, accuracy, and consistency for improved network optimization. The AI-based Cognitive Software solution also contributes to reducing carbon dioxide emissions from operational activities, for example, through the use of virtual drive-testing and remote automatic spectrum analysis. Additionally, stc Group has deployed 5G AI root-cause analysis capabilities to enable a better 5G experience for its subscribers. This future-proof deployment enables stc Group to leverage the Ericsson Performance Optimizers portfolio for surgical optimization analysis and recommendation. Ericsson Performance Optimizers use digital twin technology and advanced AI techniques like deep reinforcement learning and expert recommender systems to proactively provide mobile network optimization recommendations and resolve specific network performance issues, enabling a superior subscriber experience, while reducing operating costs.

What is meant by Bandit? True expected reward of picking arm'a'

The high emission and low energy efficiency caused by internal combustion engines (ICE) have become unacceptable under environmental regulations and the energy crisis. As a promising alternative solution, multi-power source electric vehicles (MPS-EVs) introduce different clean energy systems to improve powertrain efficiency. The energy management strategy (EMS) is a critical technology for MPS-EVs to maximize efficiency, fuel economy, and range. Reinforcement learning (RL) has become an effective methodology for the development of EMS. RL has received continuous attention and research, but there is still a lack of systematic analysis of the design elements of RL-based EMS. To this end, this paper presents an in-depth analysis of the current research on RL-based EMS (RL-EMS) and summarizes the design elements of RL-based EMS. This paper first summarizes the previous applications of RL in EMS from five aspects: algorithm, perception scheme, decision scheme, reward function, and innovative training method. The contribution of advanced algorithms to the training effect is shown, the perception and control schemes in the literature are analyzed in detail, different reward function settings are classified, and innovative training methods with their roles are elaborated. Finally, by comparing the development routes of RL and RL-EMS, this paper identifies the gap between advanced RL solutions and existing RL-EMS. Finally, this paper suggests potential development directions for implementing advanced artificial intelligence (AI) solutions in EMS.

With recent advances in the fabrication and control of hardware for quantum information processing, the possibilities of merging quantum computing (QC) with machine learning (ML) have received a huge amount of attention within the growing research community. Hereby, reinforcement learning (RL) is the third paradigm besides supervised and unsupervised learning. In this survey article, we provide an overview over so-called quantum reinforcement learning (QRL) algorithms. We understand these as quantum-assisted approaches, that solve a particular task (be they classical or quantum in nature) by employing quantum resources (either in simulation and/or in experiment). In order to keep this contribution as self-contained as possible, we provide the necessary backgrounds before venturing into the QRL literature. We start out with a brief recap of the essentials of the RL paradigm in the fully classical setting in Sec. 2. Further, in Sec. 3 we provide a quick introduction to QC and variational quantum circuits (VQCs). Readers familiar with either of the topics may safely skip these sections. In Sec. 4 we turn our attention to the emerging field of QRL, starting out with a quick overview of the literature.

In this paper, we place deep Q-learning into a control-oriented perspective and study its learning dynamics with well-established techniques from robust control. We formulate an uncertain linear time-invariant model by means of the neural tangent kernel to describe learning. We show the instability of learning and analyze the agent's behavior in frequency-domain. Then, we ensure convergence via robust controllers acting as dynamical rewards in the loss function. We synthesize three controllers: state-feedback gain scheduling H2, dynamic Hinf, and constant gain Hinf controllers. Setting up the learning agent with a control-oriented tuning methodology is more transparent and has well-established literature compared to the heuristics in reinforcement learning. In addition, our approach does not use a target network and randomized replay memory. The role of the target network is overtaken by the control input, which also exploits the temporal dependency of samples (opposed to a randomized memory buffer). Numerical simulations in different OpenAI Gym environments suggest that the Hinf controlled learning performs slightly better than Double deep Q-learning.

Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation-systems, and gaming. Similarly, graph neural networks (GNN) have also demonstrated their superior performance in supervised learning for graph-structured data. In recent times, the fusion of GNN with DRL for graph-structured environments has attracted a lot of attention. This paper provides a comprehensive review of these hybrid works. These works can be classified into two categories: (1) algorithmic enhancement, where DRL and GNN complement each other for better utility; (2) application-specific enhancement, where DRL and GNN support each other. This fusion effectively addresses various complex problems in engineering and life sciences. Based on the review, we further analyze the applicability and benefits of fusing these two domains, especially in terms of increasing generalizability and reducing computational complexity. Finally, the key challenges in integrating DRL and GNN, and potential future research directions are highlighted, which will be of interest to the broader machine learning community.