Indoor multi-robot communications face two key challenges: one is the severe signal strength degradation caused by blockages (e.g., walls) and the other is the dynamic environment caused by robot mobility. To address these issues, we consider the reconfigurable intelligent surface (RIS) to overcome the signal blockage and assist the trajectory design among multiple robots. Meanwhile, the non-orthogonal multiple access (NOMA) is adopted to cope with the scarcity of spectrum and enhance the connectivity of robots. Considering the limited battery capacity of robots, we aim to maximize the energy efficiency by jointly optimizing the transmit power of the access point (AP), the phase shifts of the RIS, and the trajectory of robots. A novel federated deep reinforcement learning (F-DRL) approach is developed to solve this challenging problem with one dynamic long-term objective. Through each robot planning its path and downlink power, the AP only needs to determine the phase shifts of the RIS, which can significantly save the computation overhead due to the reduced training dimension. Simulation results reveal the following findings: I) the proposed F-DRL can reduce at least 86% convergence time compared to the centralized DRL; II) the designed algorithm can adapt to the increasing number of robots; III) compared to traditional OMA-based benchmarks, NOMA-enhanced schemes can achieve higher energy efficiency.

Enes Bilgin works as a Principal AI Engineer and a Tech Lead at Microsoft's Autonomous Systems division, focusing on Project Bonsai. He is a machine learning and operations research practitioner and researcher and the author of a recent book "Mastering Reinforcement Learning with Python." He holds an M.S. and a Ph.D. in Systems Engineering from Boston University and a B.S. in Industrial Engineering from Bilkent University. In the past, he worked as a Research Scientist at Amazon and as an Operations Research Scientist at AMD. He also held adjunct faculty positions at the McCombs School of Business at the University of Texas at Austin and at the Ingram School of Engineering at Texas State University.

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Driving safely requires multiple capabilities from human and intelligent agents, such as the generalizability to unseen environments, the safety awareness of the surrounding traffic, and the decision-making in complex multi-agent settings. Despite the great success of Reinforcement Learning (RL), most of the RL research works investigate each capability separately due to the lack of integrated environments. In this work, we develop a new driving simulation platform called MetaDrive to support the research of generalizable reinforcement learning algorithms for machine autonomy. MetaDrive is highly compositional, which can generate an infinite number of diverse driving scenarios from both the procedural generation and the real data importing. Based on MetaDrive, we construct a variety of RL tasks and baselines in both single-agent and multi-agent settings, including benchmarking generalizability across unseen scenes, safe exploration, and learning multi-agent traffic. The generalization experiments conducted on both procedurally generated scenarios and real-world scenarios show that increasing the diversity and the size of the training set leads to the improvement of the RL agent's generalizability. We further evaluate various safe reinforcement learning and multi-agent reinforcement learning algorithms in MetaDrive environments and provide the benchmarks. Source code, documentation, and demo video are available at \url{ https://metadriverse.github.io/metadrive}.

Accuracy and stability are common requirements for Quadrotor trajectory tracking systems. Designing an accurate and stable tracking controller remains challenging, particularly in unknown and dynamic environments with complex aerodynamic disturbances. We propose a Quantile-approximation-based Distributional-reinforced Uncertainty Estimator (QuaDUE) to accurately identify the effects of aerodynamic disturbances, i.e., the uncertainties between the true and estimated Control Contraction Metrics (CCMs). Taking inspiration from contraction theory and integrating the QuaDUE for uncertainties, our novel CCM-based trajectory tracking framework tracks any feasible reference trajectory precisely whilst guaranteeing exponential convergence. More importantly, the convergence and training acceleration of the distributional RL are guaranteed and analyzed, respectively, from theoretical perspectives. We also demonstrate our system under unknown and diverse aerodynamic forces. Under large aerodynamic forces (>2m/s^2), compared with the classic data-driven approach, our QuaDUE-CCM achieves at least a 56.6% improvement in tracking error. Compared with QuaDRED-MPC, a distributional RL-based approach, QuaDUE-CCM achieves at least a 3 times improvement in contraction rate.

We present a method for finding optimal hedging policies for arbitrary However, since the idealized assumptions of a complete market initial portfolios and market states. We develop a novel actorcritic do not apply in real markets, it is not surprising that complete market algorithm for solving general risk-averse stochastic control models require constant manual adjustments and oversight, for problems and use it to learn hedging strategies across multiple risk example adjusting delta by "skew delta", smoothing barriers priced aversion levels simultaneously. We demonstrate the effectiveness with local volatility, and taking into account market impact when of the approach with a numerical example in a stochastic volatility trading vega to hedge auto-callable products.

While research in ad hoc teamwork has great potential for solving real-world robotic applications, most developments so far have been focusing on environments with simple dynamics. In this article, we discuss how the problem of ad hoc teamwork can be of special interest for marine robotics and how it can aid marine operations. Particularly, we present a set of challenges that need to be addressed for achieving ad hoc teamwork in underwater environments and we discuss possible solutions based on current state-of-the-art developments in the ad hoc teamwork literature.

TSP is a classical NP-hard combinatorial optimization problem with many practical variants. LKH is one of the state-of-the-art local search algorithms for the TSP. LKH-3 is a powerful extension of LKH that can solve many TSP variants. Both LKH and LKH-3 associate a candidate set to each city to improve the efficiency, and have two different methods, $\alpha$-measure and POPMUSIC, to decide the candidate sets. In this work, we first propose a Variable Strategy Reinforced LKH (VSR-LKH) algorithm, which incorporates three reinforcement learning methods (Q-learning, Sarsa, Monte Carlo) with LKH, for the TSP. We further propose a new algorithm called VSR-LKH-3 that combines the variable strategy reinforcement learning method with LKH-3 for typical TSP variants, including the TSP with time windows (TSPTW) and Colored TSP (CTSP). The proposed algorithms replace the inflexible traversal operations in LKH and LKH-3 and let the algorithms learn to make a choice at each search step by reinforcement learning. Both LKH and LKH-3, with either $\alpha$-measure or POPMUSIC, can be significantly improved by our methods. Extensive experiments on 236 widely-used TSP benchmarks with up to 85,900 cities demonstrate the excellent performance of VSR-LKH. VSR-LKH-3 also significantly outperforms the state-of-the-art heuristics for TSPTW and CTSP.

We utilize an offline reinforcement learning (RL) model for sequential targeted promotion in the presence of budget constraints in a real-world business environment. In our application, the mobile app aims to boost customer retention by sending cash bonuses to customers and control the costs of such cash bonuses during each time period. To achieve the multi-task goal, we propose the Budget Constrained Reinforcement Learning for Sequential Promotion (BCRLSP) framework to determine the value of cash bonuses to be sent to users. We first find out the target policy and the associated Q-values that maximizes the user retention rate using an RL model. A linear programming (LP) model is then added to satisfy the constraints of promotion costs. We solve the LP problem by maximizing the Q-values of actions learned from the RL model given the budget constraints. During deployment, we combine the offline RL model with the LP model to generate a robust policy under the budget constraints. Using both online and offline experiments, we demonstrate the efficacy of our approach by showing that BCRLSP achieves a higher long-term customer retention rate and a lower cost than various baselines. Taking advantage of the near real-time cost control method, the proposed framework can easily adapt to data with a noisy behavioral policy and/or meet flexible budget constraints.

Pushing objects through cluttered scenes is a challenging task, especially when the objects to be pushed have initially unknown dynamics and touching other entities has to be avoided to reduce the risk of damage. In this paper, we approach this problem by applying deep reinforcement learning to generate pushing actions for a robotic manipulator acting on a planar surface where objects have to be pushed to goal locations while avoiding other items in the same workspace. With the latent space learned from a depth image of the scene and other observations of the environment, such as contact information between the end effector and the object as well as distance to the goal, our framework is able to learn contact-rich pushing actions that avoid collisions with other objects. As the experimental results with a six degrees of freedom robotic arm show, our system is able to successfully push objects from start to end positions while avoiding nearby objects. Furthermore, we evaluate our learned policy in comparison to a state-of-the-art pushing controller for mobile robots and show that our agent performs better in terms of success rate, collisions with other objects, and continuous object contact in various scenarios.