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.
CODA resolves coordination issues in offline multi-agent reinforcement learning.
AgensFlow learns multi-agent coordination policies from experience.
Multi-agent coordination is prevalent in many real-world applications. However, such coordination is challenging due to its combinatorial nature. An important observation in this regard is that agents in the real world often only directly affect a limited set of neighbouring agents. Leveraging such loose couplings amon…
NeuroMAS treats multi-agent systems as neural networks for scalable, trainable coordination.
Study uses AUVs and RL to map river plumes over multiple days.
In multi-agent reinforcement learning, discovering successful collective behaviors is challenging as it requires exploring a joint action space that grows exponentially with the number of agents. While the tractability of independent agent-wise exploration is appealing, this approach fails on tasks that require elabora…
Solving tasks with sparse rewards is one of the most important challenges in reinforcement learning. In the single-agent setting, this challenge is addressed by introducing intrinsic rewards that motivate agents to explore unseen regions of their state spaces; however, applying these techniques naively to the multi-age…
New approach treats coordination as an architectural layer to improve LLM-based multi-agent systems.
A deep RL approach learns multi-agent coordination through dynamic graph communication.
ARCO-BO optimizes multi-agent design under heterogeneity, improving efficiency and performance.
MaxMax Q-Learning improves coordination in multi-agent reinforcement learning by refining action selection.
Dynamic meta-learning improves multi-agent communication with natural language.
This paper proposes a decentralized reinforcement learning method for multi-agent resource allocation.
Intrinsically motivated reinforcement learning aims to address the exploration challenge for sparse-reward tasks. However, the study of exploration methods in transition-dependent multi-agent settings is largely absent from the literature. We aim to take a step towards solving this problem. We present two exploration m…
We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay. When modeling such settings, it is often beneficial to design hierarchical models that can capture long-term coordination using intermediate variables. Furtherm…
This work benchmarks MARL algorithms in cooperative tasks.
ATLAS uses LLMs to adaptively trade by optimizing prompts and coordinating agents.
Deep reinforcement learning (DRL) is a booming area of artificial intelligence. Many practical applications of DRL naturally involve more than one collaborative learners, making it important to study DRL in a multi-agent context. Previous research showed that effective learning in complex multi-agent systems demands fo…
Recent developments in deep reinforcement learning are concerned with creating decision-making agents which can perform well in various complex domains. A particular approach which has received increasing attention is multi-agent reinforcement learning, in which multiple agents learn concurrently to coordinate their ac…
Real-time advertising allows advertisers to bid for each impression for a visiting user. To optimize specific goals such as maximizing revenue and return on investment (ROI) led by ad placements, advertisers not only need to estimate the relevance between the ads and user's interests, but most importantly require a str…
Proposes a graph neural network for efficient multi-agent routing.
New algorithms allow multiple robots to search efficiently without central coordination.
FACMAC combines deep policy gradients with factored critic for multi-agent reinforcement learning.
The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them. Here we introduce Relational Forward Models (RFM) for multi-agent learning, networks that can learn to make accurate predic…
Paper tackles delays in multi-agent reinforcement learning, improving performance.
New methods improve multi-agent reinforcement learning by addressing rotational dynamics.
Flexible decentralized MARL framework for cooperative multi-agent learning.
Many potential applications of reinforcement learning in the real world involve interacting with other agents whose numbers vary over time. We propose new neural policy architectures for these multi-agent problems. In contrast to other methods of training an individual, discrete policy for each agent and then enforcing…
This paper presents a novel deep reinforcement learning-based resource allocation technique for the multi-agent environment presented by a cognitive radio network that coexists through underlay dynamic spectrum access (DSA) with a primary network. The resource allocation technique presented in this work is distributed,…
Deep reinforcement learning algorithms have recently been used to train multiple interacting agents in a centralised manner whilst keeping their execution decentralised. When the agents can only acquire partial observations and are faced with tasks requiring coordination and synchronisation skills, inter-agent communic…
RD-Agent(Q) automates quantitative finance research and development.
Adapts agent strategies on-the-fly for better cross-play in cooperative settings.
QMIX combines per-agent values to create decentralised policies.
In the last few years, deep multi-agent reinforcement learning (RL) has become a highly active area of research. A particularly challenging class of problems in this area is partially observable, cooperative, multi-agent learning, in which teams of agents must learn to coordinate their behaviour while conditioning only…
Novel ML approach solves complex warehouse routing problem.
New framework predicts cryptocurrency trends by analyzing news and market data.
We propose a targeted communication architecture for multi-agent reinforcement learning, where agents learn both what messages to send and whom to address them to while performing cooperative tasks in partially-observable environments. This targeting behavior is learnt solely from downstream task-specific reward withou…
KVCOMM optimizes multi-agent LLM systems by reusing KV-caches, reducing redundant processing.
Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocurriculum inducing multiple distinct rounds of emergent strategy, many of which require sophisticated tool use and coordination. We find clea…
Human players in professional team sports achieve high level coordination by dynamically choosing complementary skills and executing primitive actions to perform these skills. As a step toward creating intelligent agents with this capability for fully cooperative multi-agent settings, we propose a two-level hierarchica…
DePAint solves MARL for agents with local constraints, privacy, and no central controller.
Enhances anomaly detection in financial markets using AI agents.
We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions. Causal influence is assessed using counterfactual reasoning. At each timestep, an agent simulates alternate actions t…
Effective coordination is crucial to solve multi-agent collaborative (MAC) problems. While centralized reinforcement learning methods can optimally solve small MAC instances, they do not scale to large problems and they fail to generalize to scenarios different from those seen during training. In this paper, we conside…
JPS improves joint policies for multi-agent collaboration in imperfect information games.
Most of the prior work on multi-agent reinforcement learning (MARL) achieves optimal collaboration by directly controlling the agents to maximize a common reward. In this paper, we aim to address this from a different angle. In particular, we consider scenarios where there are self-interested agents (i.e., worker agent…
A new MARL framework for community-based cooperation with transfer and active exploration.
A framework for multi-agent communication over noisy channels in reinforcement learning.