A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Examines multiagent systems for complex learning tasks.
Proposes a model to estimate treatment effects in complex multiagent systems over time.
Modeling agent behavior is central to understanding the emergence of complex phenomena in multiagent systems. Prior work in agent modeling has largely been task-specific and driven by hand-engineering domain-specific prior knowledge. We propose a general learning framework for modeling agent behavior in any multiagent …
Progress in multiagent intelligence research is fundamentally limited by the number and quality of environments available for study. In recent years, simulated games have become a dominant research platform within reinforcement learning, in part due to their accessibility and interpretability. Previous works have targe…
End-to-end model predicts multiagent trajectories using game theory and neural nets.
New algorithm breaks multiagency gap in robust MARL.
Multiagent reinforcement learning algorithms (MARL) have been demonstrated on complex tasks that require the coordination of a team of multiple agents to complete. Existing works have focused on sharing information between agents via centralized critics to stabilize learning or through communication to increase perform…
Optimization of parameterized policies for reinforcement learning (RL) is an important and challenging problem in artificial intelligence. Among the most common approaches are algorithms based on gradient ascent of a score function representing discounted return. In this paper, we examine the role of these policy gradi…
V-learning tackles multiagent reinforcement learning by reducing sample complexity.
New algorithms for RL in Markov games with independent linear function approximation, breaking the curse of multiagents.
Datasets with hundreds to tens of thousands features is the new norm. Feature selection constitutes a central problem in machine learning, where the aim is to derive a representative set of features from which to construct a classification (or prediction) model for a specific task. Our experimental study involves micro…
In this paper, we predict the likelihood of a player making a shot in basketball from multiagent trajectories. Previous approaches to similar problems center on hand-crafting features to capture domain specific knowledge. Although intuitive, recent work in deep learning has shown this approach is prone to missing impor…
New MARL algorithms resolve the curse of multiagency with function approximation.
Many cooperative multiagent reinforcement learning environments provide agents with a sparse team-based reward, as well as a dense agent-specific reward that incentivizes learning basic skills. Training policies solely on the team-based reward is often difficult due to its sparsity. Furthermore, relying solely on the a…
The scale of Internet-connected systems has increased considerably, and these systems are being exposed to cyber attacks more than ever. The complexity and dynamics of cyber attacks require protecting mechanisms to be responsive, adaptive, and scalable. Machine learning, or more specifically deep reinforcement learning…
The emergence of complex life on Earth is often attributed to the arms race that ensued from a huge number of organisms all competing for finite resources. We present an artificial intelligence research environment, inspired by the human game genre of MMORPGs (Massively Multiplayer Online Role-Playing Games, a.k.a. MMO…
Paper develops a private algorithm for multi-agent learning in bandits.
Toward enabling next-generation robots capable of socially intelligent interaction with humans, we present a of interactions in a social environment of multiple agents and multiple groups. The Multiagent Group Perception and Interaction (MGpi) network is a deep neural network that predi…
A new multiagent model of the stock market is formulated that contains four states in which the agents may be located. Next, the model is reformulated in the language of the functional integral containing fluctuations of prices and quantities of cash flows. It is shown that in the functional integral of that type descr…
Paper shows RLHF can be solved similarly to standard RL.
This paper is intended to explain, in simple terms, some of the mechanisms and agents common to multiagent financial market simulations. We first discuss the necessity to include an exogenous price time series ("the fundamental value") for each asset and three methods for generating that series. We then illustrate one …
MARL improves LBM stability and accuracy across scales.
In this paper it was developed a modification of the known multiagent model Minority Game, designed to simulate the behavior of traders in financial markets and the resulting price dynamics on the abstract resource. The model was implemented in the form of software. The modified version of Minority Game was investigate…
We consider the problem of belief aggregation: given a group of individual agents with probabilistic beliefs over a set of uncertain events, formulate a sensible consensus or aggregate probability distribution over these events. Researchers have proposed many aggregation methods, although on the question of which is be…
New method combines value function decomposition and policy gradients for cooperative multi-agent reinforcement learning.
We discuss the behavior of two magnitudes, physical complexity and mutual information function of the outcome of a model of heterogeneous, inductive rational agents inspired in the El Farol Bar problem and the Minority Game. The first is a measure rooted in Kolmogorov-Chaitin theory and the second one a measure related…
Learning when to communicate and doing that effectively is essential in multi-agent tasks. Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks. In this paper, we present Individualized Controlled Co…
Describes state variables in sequential decision problems, linking them to Markovian and non-Markovian models.
Machine learning has begun to play a central role in many applications. A multitude of these applications typically also involve datasets that are distributed across multiple computing devices/machines due to either design constraints (e.g., multiagent systems) or computational/privacy reasons (e.g., learning on smartp…
Deep reinforcement learning has achieved many recent successes, but our understanding of its strengths and limitations is hampered by the lack of rich environments in which we can fully characterize optimal behavior, and correspondingly diagnose individual actions against such a characterization. Here we consider a fam…
Paper develops efficient algorithms for learning rationalizable equilibria in multiplayer games.
EMIX minimizes surprise in multi-agent reinforcement learning.
We solve continuous-time reinforcement learning using distributional Hamilton-Jacobi-Bellman equations.
Policy gradient and actor-critic algorithms form the basis of many commonly used training techniques in deep reinforcement learning. Using these algorithms in multiagent environments poses problems such as nonstationarity and instability. In this paper, we first demonstrate that standard softmax-based policy gradient c…
SONATA algorithm converges to solutions of nonconvex smooth functions with KL property.
In this paper, we consider a class of finite-sum convex optimization problems defined over a distributed multiagent network with agents connected to a central server. In particular, the objective function consists of the average of () smooth components associated with each network agent together with a s…
Overview of integrable systems with symmetries, focusing on toric and semitoric systems.
New method to derive integrable systems from existing Lax systems.
Learning to control linear systems is statistically hard, especially for underactuated systems.
Discrete-time systems can be characterized by simple flat coordinates and their shifts.
The paper explores when linear system identification is hard or easy, especially for under-actuated systems.
This paper improves system identification by reducing sample complexity for high-dimensional linear dynamical systems.
In integrable hydrodynamic systems, coordinates exist where generators and symmetries are simple.
This paper studies nonholonomic constraints in Hamiltonian systems, deriving equations and theorems.
New method models unknown systems with hidden parameters using neural networks.
Study absolute equivalence for Pfaffian systems, applying to control systems.
Solves selecting the best optimizing system problems.
This paper considers control systems defined on Lie algebroids. After deriving basic controllability tests for general control systems, we specialize our discussion to the class of mechanical control systems on Lie algebroids. This class of systems includes mechanical systems subject to holonomic and nonholonomic const…