arXiv research

Method models other agents' behaviors without requiring direct observation.

Agent-to-agent finance aims to manage payments and trust for AI agents.

AI agents manage portfolios, improving on human oversight.

RL agents outperform baselines in asset allocation.

Agents learn to give rewards to others in a shared learning environment.

New algorithm reduces regret in multi-agent bandits with malicious agents.

Regret analysis is challenging in Multi-Agent Reinforcement Learning (MARL) primarily due to the dynamical environments and the decentralized information among agents. We attempt to solve this challenge in the context of decentralized learning in multi-agent linear-quadratic (LQ) dynamical systems. We begin with a simp…

We formulate and analyze a multi-agent model for the evolution of individual and systemic risk in which the local agents interact with each other through a central agent who, in turn, is influenced by the mean field of the local agents. The central agent is stabilized by a bistable potential, the only stabilizing force…

Algorithm maximizes total reward in multi-agent bandits with adversarial corruptions.

I2C enables agents to learn efficient communication without redundancy.

We present an effective technique for training deep learning agents capable of negotiating on a set of clauses in a contract agreement using a simple communication protocol. We use Multi Agent Reinforcement Learning to train both agents simultaneously as they negotiate with each other in the training environment. We al…

PEAR dynamically reconfigures agent roles to prevent persistent biases in multi-agent debates.

In mix-game which is an extension of minority game, there are two groups of agents; group1 plays the majority game, but the group2 plays the minority game. This paper studies the change of the average winnings of agents and volatilities vs. the change of mixture of agents in mix-game model. It finds that the correlatio…

We propose a method for modeling and learning turn-taking behaviors for accessing a shared resource. We model the individual behavior for each agent in an interaction and then use a multi-agent fusion model to generate a summary over the expected actions of the group to render the model independent of the number of age…

Many learning agents impact a financial market model, showing complex dynamics.

Reinforcement learning (RL) algorithms allow agents to learn skills and strategies to perform complex tasks without detailed instructions or expensive labelled training examples. That is, RL agents can learn, as we learn. Given the importance of learning in our intelligence, RL has been thought to be one of key compone…

Agents collaborate to reduce regret in a multi-agent linear bandit problem with side information.

Adapts agent strategies on-the-fly for better cross-play in cooperative settings.

A novel framework uses goal-conditioned reinforcement learning to generate diverse samples.

Agents need world models to generalize multi-step tasks.

ODC protocol improves learning in asynchronous multi-agent bandits.

The ability of modeling the other agents, such as understanding their intentions and skills, is essential to an agent's interactions with other agents. Conventional agent modeling relies on passive observation from demonstrations. In this work, we propose an interactive agent modeling scheme enabled by encouraging an a…

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…

EPC curriculum improves MARL performance as agent population grows.

Agents learn social skills from each other, improving performance.

This paper presents an analytical treatment of economic systems with an arbitrary number of agents that keeps track of the systems' interactions and agents' complexity. This formalism does not seek to aggregate agents. It rather replaces the standard optimization approach by a probabilistic description of both the enti…

Study uses AI agents to improve equity portfolio management.

Study improves online learning with adaptable agents in various settings.

Kernel method improves cooperative decision-making among agents.

This work formalizes and extends parameter sharing in multi-agent reinforcement learning.

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…

A simple learning agent learns to trade in an agent-based market model.

This paper addresses reward estimation and incentive design for agents with hidden rewards.

Study of repeated principal-agent bandit game with self-interested and exploratory learning agents.

MARLA uses deep reinforcement learning for multi-agent AHT, reducing Bayes risk.

Study on multi-agent decision making complexity, showing sample efficiency gaps.

In this paper, we explore using deep reinforcement learning for problems with multiple agents. Most existing methods for deep multi-agent reinforcement learning consider only a small number of agents. When the number of agents increases, the dimensionality of the input and control spaces increase as well, and these met…

A financial market model uses spin variables to represent and predict agent behavior.

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…

Study collaborative learning among multi-agents in multi-armed bandits.

MARL algorithm uses regularization to avoid explicit structures, improving performance.

We propose a method to efficiently learn diverse strategies in reinforcement learning for query reformulation in the tasks of document retrieval and question answering. In the proposed framework an agent consists of multiple specialized sub-agents and a meta-agent that learns to aggregate the answers from sub-agents to…

This paper extends off-policy reinforcement learning to the multi-agent case in which a set of networked agents communicating with their neighbors according to a time-varying graph collaboratively evaluates and improves a target policy while following a distinct behavior policy. To this end, the paper develops a multi-…

New learning methods for open systems with variable agents.

AGENTICAITA uses AI agents to autonomously trade markets without human intervention.

Dynamic meta-learning improves multi-agent communication with natural language.

Enhances financial analysis with multi-agent collaboration.

While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box. We propose to create a shared agent-entity graph, where agents and environmental entities form vertices, and edges exist between the vertices which can communicate with each other. Agent…