Abstract We study a game between liquidity provider (LP) and liquidity taker agents interacting in an over‐the‐counter market, for which the typical example is foreign exchange. We show how a suitable design of parameterized families of reward functions coupled with shared policy learning constitutes an efficient solution to this problem. By playing against each other, our deep‐reinforcement‐learning‐driven agents learn emergent behaviors relative to a wide spectrum of objectives encompassing profit‐and‐loss, optimal execution, and market share. In particular, we find that LPs naturally learn to balance hedging and skewing, where skewing refers to setting their buy and sell prices asymmetrically as a function of their inventory. We further introduce a novel RL‐based calibration algorithm, which we found performed well at imposing constraints on the game equilibrium. On the theoretical side, we are able to show convergence rates for our multi‐agent policy gradient algorithm under a transitivity assumption, closely related to generalized ordinal potential games.
In this paper, we propose a general framework, DEMiR-CF, for a multi-robot team to achieve a complex mission including inter-related tasks that require diverse capabilities and/or simultaneous executions. Our framework integrates a distributed task allocation scheme, cooperation mechanisms and precaution routines for multi-robot team execution. Its performance has been demonstrated in NavalMine Countermeasures, Multi-robotMulti-Target Exploration and Object Construction domains. The framework not only ensures near-optimal solutions for task achievement but also efficiently responds to real time contingencies.
Adoption of AI by criminal entities across traditional and emerging financial crime paradigms has been a disturbing recent trend. Particularly concerning is the proliferation of generative AI, which has empowered criminal activities ranging from sophisticated phishing schemes to the creation of hard-to-detect deep fakes, and to advanced spoofing attacks to biometric authentication systems. The exploitation of AI by criminal purposes continues to escalate, presenting an unprecedented challenge. AI adoption causes an increasingly complex landscape of fraud typologies intertwined with cybersecurity vulnerabilities. Overall, GenAI has a transformative effect on financial crimes and fraud. According to some estimates, GenAI will quadruple the fraud losses by 2027 with a staggering annual growth rate of over 30% [27]. As crime patterns become more intricate, personalized, and elusive, deploying effective defensive AI strategies becomes indispensable. However, several challenges hinder the necessary progress of AI-based fincrime detection systems. This paper examines the latest trends in AI/ML-driven financial crimes and detection systems. It underscores the urgent need for developing agile AI defenses that can effectively counteract the rapidly emerging threats. It also aims to highlight the need for cooperation across the financial services industry to tackle the GenAI induced crime waves.
Hybrid deliberative-reactive control architectures are a popular and effective approach to the control of robotic navigation applications. However, the design of said architectures is difficult, due to the fundamental differences in the design of the reactive and deliberative layers of the architecture. We propose a novel approach to improving system-level performance of said architectures, by improving the deliberative layer's model of the reactive layer's execution of its plans through the use of machine learning techniques. Quantitative and qualitative results from a physics-based simulator are presented.
Robot Competition and Exhibition. A decade of contests and exhibitions have inspired innovation and research in AI robotics. Here we look back at the origins of the contest and how it evolved. We also reflect on how the contest has served as an arena for important debates in the AI and robotics communities. The article closes with a speculative look forward to the next decade of AAAI robot competitions.
In electronic trading markets often only the price or volume time series, that result from interaction of multiple market participants, are directly observable. In order to test trading strategies before deploying them to real-time trading, multi-agent market environments calibrated so that the time series that result from interaction of simulated agents resemble historical are often used. To ensure adequate testing, one must test trading strategies in a variety of market scenarios -- which includes both scenarios that represent ordinary market days as well as stressed markets (most recently observed due to the beginning of COVID pandemic). In this paper, we address the problem of multi-agent simulator parameter calibration to allow simulator capture characteristics of different market regimes. We propose a novel two-step method to train a discriminator that is able to distinguish between real and fake price and volume time series as a part of GAN with self-attention, and then utilize it within an optimization framework to tune parameters of a simulator model with known agent archetypes to represent a market scenario. We conclude with experimental results that demonstrate effectiveness of our method.
Simulated environments are increasingly used by trading firms and investment banks to evaluate trading strategies before approaching real markets. Backtesting, a widely used approach, consists of simulating experimental strategies while replaying historical market scenarios. Unfortunately, this approach does not capture the market response to the experimental agents' actions. In contrast, multi-agent simulation presents a natural bottom-up approach to emulating agent interaction in financial markets. It allows to set up pools of traders with diverse strategies to mimic the financial market trader population, and test the performance of new experimental strategies. Since individual agent-level historical data is typically proprietary and not available for public use, it is difficult to calibrate multiple market agents to obtain the realism required for testing trading strategies. To addresses this challenge we propose a synthetic market generator based on Conditional Generative Adversarial Networks (CGANs) trained on real aggregate-level historical data. A CGAN-based "world" agent can generate meaningful orders in response to an experimental agent. We integrate our synthetic market generator into ABIDES, an open source simulator of financial markets. By means of extensive simulations we show that our proposal outperforms previous work in terms of stylized facts reflecting market responsiveness and realism.
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