Method models other agents' behaviors without requiring direct observation.
problem Understanding and interacting effectively with other agents in reinforcement learning.
method Extracts representations from local observations of the controlled agent using encoder-decoder architectures.
result The method achieves higher returns than baseline methods in multi-agent environments.
Agent-to-agent finance aims to manage payments and trust for AI agents.
problem Managing financial interactions between autonomous AI agents.
method Develops agent-to-agent finance concept and explores blockchain solutions.
result Agent-to-agent finance can address coordination frictions in financial markets.
AI agents manage portfolios, improving on human oversight.
problem Improving strategic asset allocation for institutional investors.
method 50 specialized agents produce capital market assumptions, construct portfolios, critique, and vote on each other's output.
result Meta-agent compares forecasts with realized returns and improves agent performance.
New algorithm reduces learning regret in multi-agent systems with unknown dynamics.
problem Challenges in decentralized learning due to unknown dynamics and lack of communication.
method Proposed MARL algorithm for two-agent LQ systems with unknown dynamics and one-directional communication.
result Achieved O ( T ) O(\sqrt{T}) O ( T ) regret bound for multi-agent LQ systems with certain communication patterns. RL agents outperform baselines in asset allocation.
problem Optimizing asset allocation using reinforcement learning.
method Model-free deep RL agents trained on real-world stock prices.
result RL agents significantly outperformed random and uniform allocation.
Agents learn to give rewards to others in a shared learning environment.
problem How to encourage cooperation among RL agents in a shared environment.
method Each agent learns a reward function to influence others, optimizing for its own and others' extrinsic objectives.
result Agents significantly outperform standard RL in Markov games, often finding near-optimal division of labor.
Deep learning agents negotiate contracts with prosocial or selfish behaviors.
problem Training agents to negotiate contracts with varying behaviors.
method Multi-Agent Reinforcement Learning, modeling prosocial and selfish behaviors, training a meta agent.
result Trained agents hold their own against human players and emulate human behavior.
RFM models predict multi-agent behavior, offering insights and faster learning.
problem Understanding and improving multi-agent systems learning.
method Relational Forward Models (RFM) that learn to predict future agent behavior.
result RFM modules inside agents lead to faster learning compared to baseline methods.
New algorithm reduces regret in multi-agent bandits with malicious agents.
problem Collaboration between honest and malicious agents in multi-armed bandits.
method Dynamic reduction of communication with malicious agents, learning who is malicious.
result Algorithm reduces regret even with a single malicious agent, assuming m m m is small compared to K K K . New method makes deep RL agents more understandable.
problem Incomprehensible decision-making in NN-based RL agents limits their applications.
method Derives a secondary comprehensible agent from a NN-based RL agent.
result Empirical evaluation supports the possibility of building a comprehensible agent.
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…
Interactive agent modeling by learning to probe improves understanding of other agents' behaviors.
problem Understanding and predicting the behaviors of other agents in interactive scenarios.
method An interactive agent modeling scheme enabled by encouraging the agent to learn to probe, combining imitation learning and curiosity-driven reinforcement learning.
result The agent model learned by the proposed approach generalizes better and enhances performance in multiple applications.
Algorithm maximizes total reward in multi-agent bandits with adversarial corruptions.
problem Maximizing total reward in multi-agent bandits with adversarial corruptions.
method Proposes a cooperative learning algorithm robust to adversarial corruptions.
result Demonstrates an additive O ( ( L / L min ) C ) O((L / L_{\min}) C) O (( L / L m i n ) C ) regret term for an adversary with unknown corruption budget. I2C enables agents to learn efficient communication without redundancy.
problem Redundant broadcast communication in multi-agent cooperation.
method I2C learns a prior for agent-agent communication via causal inference and reinforcement learning.
result I2C reduces communication overhead and improves multi-agent cooperative performance.
New algorithm for multi-agent reinforcement learning scales with number of agents.
problem Existing methods for deep multi-agent reinforcement learning struggle with increasing number of agents.
method Proposes a distributed optimization approach assuming policies of agents are close in parameter space.
result Demonstrates superior performance on co-operative and competitive tasks compared to existing methods.
Modeling and learning turn-taking behaviors in multi-agent systems.
problem Modeling and predicting turn-taking behaviors in dynamic multi-agent systems.
method Individual behavior models (WFSTs) and multi-agent fusion model (logistic regression classifier).
result Accurately models and predicts turn-taking behaviors with high precision.
PEAR dynamically reconfigures agent roles to prevent persistent biases in multi-agent debates.
problem Persistent positional biases and sensitivity to role assignments in fixed topologies.
method Dynamic reconfiguration of agent roles and sparse topologies based on evolving agent states.
result Significantly improves average accuracy over debate baselines across multiple reasoning benchmarks.
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…
Enactive learning shows agents can learn from their environment, but limited by action choices.
problem Learning and interaction of autonomous agents in complex environments.
method Simulation of artificial agents in maze environments, comparing enactive learning to classical reinforcement learning.
result Enactive agents can learn to avoid unfavorable interactions but performance is limited by action choices.
Many learning agents impact a financial market model, showing complex dynamics.
problem Understanding the dynamics of financial markets with multiple learning agents.
method Agent-based model of financial market with multiple reinforcement learning agents interacting.
result Inclusion of learning agents changes market dynamics to match empirical data.
Agents collaborate to reduce regret in a multi-agent linear bandit problem with side information.
problem Reducing regret in a multi-agent stochastic linear bandit with side information.
method A decentralized algorithm where agents communicate subspace indices and each plays a projected LinUCB on the corresponding low-dimensional subspace.
result Per-agent finite-time regret is much smaller when agents communicate compared to non-communicating case.
A method for efficient reinforcement learning query reformulation.
problem Efficiently learn diverse strategies for query reformulation.
method A framework with specialized sub-agents and a meta-agent trained on full data.
result Improved generalization performance and diversity of reformulation strategies.
Adapts agent strategies on-the-fly for better cross-play in cooperative settings.
problem Cross-play issues between self-play agents and unseen partners.
method Adapts agent strategies using posterior belief updates via Gibbs sampling.
result Achieves strong cross-play in the Hanabi game without prior knowledge of partners' strategies.
Agents are rewarded for influencing others' actions in MARL, improving coordination and communication.
problem Achieving effective coordination and communication in Multi-Agent Reinforcement Learning.
method Rewarding agents for having causal influence over other agents' actions, assessed through counterfactual reasoning.
result Influence rewards lead to enhanced coordination and communication in challenging social dilemma environments.
This paper uses a path integral approach to model complex economic systems with many agents.
problem Modeling economic systems with a large number of interacting agents.
method Develops a path integral formalism to describe the behavior of a large number of agents in an economic system.
result The method provides an analytical treatment of business cycle models with many agents, revealing various phases and interactions.
A novel framework uses goal-conditioned reinforcement learning to generate diverse samples.
problem Generating high-quality, diverse samples from generative models.
method Two agents: GC-agent learns to reconstruct the training set, S-agent learns to imitate GC-agent without knowing the goals.
result Empirically, the method generates diverse and high-quality samples in image synthesis.
Agents need world models to generalize multi-step tasks.
problem The necessity of world models for flexible, goal-directed behavior.
method Formal analysis and demonstration of the necessity of world models for agents to generalize multi-step tasks.
result World models are necessary for agents to generalize to multi-step goal-directed tasks.
Paper develops a new multi-agent reinforcement learning algorithm.
problem Improving policies in a network of communicating agents.
method Develops a multi-agent off-policy actor-critic algorithm using emphatic temporal difference learning.
result Proves convergence of the algorithm under linear function approximation.
ODC protocol improves learning in asynchronous multi-agent bandits.
problem Cooperative multi-agent bandits with unknown, irregular pull times.
method ODC protocol for on-demand communication between agents.
result ODC protocol reduces communication complexity based on empirical pull times.
New neural policies learn multi-agent relationships directly, improving coordination in dynamic environments.
problem Training coordination among varying numbers of agents in reinforcement learning.
method Attentional architecture for shared policies that adapt to each agent's context.
result Superior performance on multi-agent vehicle coordination problem, especially with many agents.
EPC curriculum improves MARL performance as agent population grows.
problem Challenges in learning good policies for large multi-agent systems.
method Evolutionary Population Curriculum (EPC) for scaling MARL.
result EPC consistently outperforms baselines as agent population increases.
Agents learn social skills from each other, improving performance.
problem Independent RL agents fail to use social learning.
method Imposed constraints, model-based auxiliary loss, mixed training.
result Agents trained with social learning outperform solo-trained agents.
Algorithm helps first agent learn to collaborate with adaptive second agent in MDPs.
problem Designing a learning algorithm for the first agent to collaborate with an adaptive second agent in MDPs.
method Novel online learning algorithms for the first agent with a specific regret bound.
result Sub-linear regret of the first agent implies near-optimality of the joint return for smooth MDPs.
Study uses AI agents to improve equity portfolio management.
problem Improving stock selection and portfolio management efficiency.
method Role-based multi-agent systems for equity research.
result Multi-agent approach outperforms benchmarks in stock selection.
Study improves online learning with adaptable agents in various settings.
problem Learning with improving agents in online settings.
method Extensive analysis of combinatorial dimensions, multiclass setup, bandit feedback, and agent cost.
result Characterization and analysis of online learnability in the model.
This paper surveys methods to handle non-stationarity in multi-agent deep reinforcement learning.
problem Non-stationarity in multi-agent reinforcement learning environments.
method Modifications in training procedures, opponent policy representation learning, meta-learning, communication, and decentralized learning.
result A comprehensive review of recent works on addressing non-stationarity in multi-agent deep reinforcement learning.
New algorithm for multi-agent reinforcement learning with attention mechanism.
problem Challenges in training decentralized policies in multi-agent settings.
method Actor-attention-critic algorithm with centrally computed critics and attention mechanism.
result More effective and scalable learning in complex multi-agent environments.
Kernel method improves cooperative decision-making among agents.
problem Cooperative multi-agent decision making with contextual information.
method Proposed extsc{Coop-KernelUCB} algorithm for near-optimal per-agent regret.
result Near-optimal bounds on per-agent regret with efficient computation and communication.
This work formalizes and extends parameter sharing in multi-agent reinforcement learning.
problem Parameter sharing limits multi-agent learning to a single policy, preventing different tasks or action spaces.
method Introduces agent indication and extends parameter sharing to heterogeneous observation and action spaces.
result Proves convergence to optimal policies for parameter sharing in heterogeneous environments.
Agents learn to cooperate by exchanging messages in a shared graph model.
problem Creating effective multi-agent cooperation in unknown environments.
method Shared agent-entity graph, multi-agent reinforcement learning, invariant to team size and permutation.
result Decentralized multi-agent systems can quickly transfer learned policies to different team sizes.
Adaptive policies for multi-agent RL improve adaptiveness in changing environments.
problem Non-stationarity in multi-agent reinforcement learning where other agents may alter their policies.
method Train multiple adaptive policies for each agent and a policy predictor to select the best policy at execution time.
result Agents trained with our method outperform state-of-the-art methods in all tested environments.
A simple learning agent learns to trade in an agent-based market model.
problem Optimal execution of trades in an agent-based financial market model.
method Asynchronous trading through a matching engine, varying initial order sizes and state spaces, calibration of empirical stylized facts and price impact curves.
result Smaller state space agents converge faster in learning and can trade intuitively using spread and volume states.
This paper addresses reward estimation and incentive design for agents with hidden rewards.
problem Estimating and incentivizing agents with unknown rewards in a learning setting.
method Repeated adverse selection game with a self-interested learning agent and a learning principal. Introduces an estimator for consistent reward estimation and a data-driven incentive policy.
result Finite-sample consistency of the estimator and a rigorous regret bound for the principal.
MARLA uses deep reinforcement learning for multi-agent AHT, reducing Bayes risk.
problem Decentralized active hypothesis testing among multiple agents.
method MARLA algorithm using deep multi-agent reinforcement learning.
result MARLA outperforms single-agent learning in AHT problems.
Study on multi-agent decision making complexity, showing sample efficiency gaps.
problem Understanding sample efficiency in multi-agent decision making.
method General framework for interactive decision making, focusing on equilibrium computation.
result No 'reasonable' complexity measure can close gaps between single and multiple agents.
Study of repeated principal-agent bandit game with self-interested and exploratory learning agents.
problem Interaction between principal and agent in unknown environments with learning and exploration behaviors.
method Developed algorithms for self-interested and exploratory learning agents with bandit feedback, achieving regret bounds.
result Achieved O ~ ( T 2 / 3 ) \widetilde{O}(T^{2/3}) O ( T 2/3 ) regret bound for exploratory learning agent in i.i.d. reward setup. Explains financial market simulation mechanisms and agent behaviors.
problem Necessity of including fundamental value in multiagent financial market simulations.
method Discusses three methods for generating fundamental value and illustrates one Bayesian agent's estimation process.
result Presentation of two widely examined agents: Zero Intelligence and Heuristic Belief Learning.
This paper proposes using variational autoencoders to model opponents in multi-agent systems.
problem Understanding and interacting with opponents in multi-agent systems.
method Variational autoencoders for opponent modeling, with a modification to use local information.
result Our opponent modeling methods achieve equal or greater episodic returns.