Extends Optimal Transport to multiple agents, aiming for equitable and optimal distribution.
problem Sharing costs or goods equitably among multiple agents with different preferences.
method Minimizes the maximum transportation cost or maximizes the minimum utility.
result Provides a new algorithm faster than standard linear programming.
PPO algorithm converges to global optimality in multi-agent reinforcement learning.
problem Designing statistical guarantees for policy optimization methods in multi-agent reinforcement learning.
method Leveraging a multi-agent performance difference lemma, a localized action value function is used as a descent direction for each local policy, leading to a multi-agent PPO algorithm.
result The multi-agent PPO algorithm converges to the globally optimal policy at a sublinear rate under standard regularity conditions.
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.
This study analyzes mutual influence on investment strategies of financial market agents.
problem Mutual influence among agents in financial markets and its impact on investment strategies.
method Formulated optimal investment differential game problem, derived analytical solutions, proposed fast algorithm, and theoretically analyzed mutual influence.
result Agents' optimal strategies converge to the asymptotic strategy when mutual influence is strong and approaches infinity.
Algorithm learns optimal contracts for unaware principals.
problem Learning optimal contracts when principal is unaware of agent's utility and action space.
method Sequential contract offers with observed outcomes, using algorithm to approximate optimal contract.
result Algorithm learns optimal contract within epsilon of optimal net profit with bounded samples.
Optimal algorithm found for collaborative learning in bandits with optimal regret bounds.
problem Minimizing regret in collaborative multi-agent bandit problems.
method Proposed an algorithm with optimal regret bounds for collaborative multi-agent multi-armed bandit model.
result First algorithm with order optimal regret bounds for collaborative bandit model.
Optimal contract found for risk averse agent and principal with unknown quality.
problem Finding an optimal contract for a risk averse agent and principal with unknown quality.
method Continuous time Principal-Agent model with exponential utility, moral hazard, and filtering of quality.
result Explicit solution to the optimal contract problem.
Study time-inconsistent portfolio optimization for competitive agents with relative performance criteria.
problem Time-inconsistent mean field and n-agent games under relative performance criteria.
method Construct open-loop equilibrium strategies for n-agent games and mean field games.
result Explicit solutions for n-agent games and mean field games, unique in a special class of equilibria.
Study competitive agents' optimal consumption and investment strategies with relative performance criteria.
problem Optimizing consumption and investment strategies for multiple agents with relative performance considerations.
method Derived a closed-form solution for an n n n -player game and mean field game, analyzing the impact of risk tolerance and competitiveness parameters. result Unique equilibria found, showing nonlinear and non-monotone dependence on agents' risk tolerance and competitiveness parameters.
New method uses LP to achieve optimal sample complexity in multi-agent reinforcement learning.
problem Achieving global optimality in multi-agent reinforcement learning with average-cost criterion.
method Randomized Linear Programming and Stochastic Primal-Dual Methods for multi-agent saddle point problems.
result Sample complexity matches tight dependencies on state and action spaces, and scales with network size.
Study optimal growth strategies in a continuous-time asset market.
problem Guaranteeing that individual agent strategies cannot outperform the market.
method Mean-field approximation of an infinite number of infinitesimal agents, focusing on optimal strategy distribution among assets.
result Optimal strategy for market agents is to invest proportionally to discounted expected relative dividend intensities.
Algorithm learns optimal coordination for strategic agents in uncertain settings.
problem Optimizing rewards for strategic agents with private types and actions.
method Combines delaying mechanism, reward angle estimation, and LinUCB algorithm.
result Near optimal regret bound of O ~ ( T ) \tilde{O}(\sqrt{T}) O ~ ( T ) for learning optimal policy. Optimal penalties for RECs balance environmental and revenue impacts.
problem Optimizing penalties for RECs to balance environmental and revenue impacts.
method Mean field games and extended McKean-Vlasov control problems.
result Optimal penalty function is linear in agents' state.
M 3 ^3 3 RL trains a manager to infer worker minds and assign tasks for optimal collaboration.
problem Optimal coordination among self-interested agents with diverse preferences and skills.
method Mind-aware Multi-agent Management Reinforcement Learning (M^3RL) that infers worker minds and assigns tasks.
result Effective in modeling worker minds and achieving optimal ad-hoc teaming.
Deep neural net solves multi-agent optimal trading problem.
problem Optimal trade execution for multiple agents and assets.
method Residual U-net with self-attention for viscosity solution approximation.
result Neural network approach outperforms finite difference methods.
Reinforcement learning optimizes market making decisions.
problem Optimizing market making decisions in cryptocurrency markets.
method Multi-agent reinforcement learning framework with two agents: macro-agent and micro-agent.
result The proposed framework leads to successful trades in the Bitcoin market.
The paper introduces a health-informed policy gradient method for multi-agent reinforcement learning.
problem Optimizing joint reward functions in multi-agent systems with varying agent health.
method Health-informed credit assignment in a multi-agent proximal policy optimization algorithm.
result Significant improvement in learning performance compared to traditional methods.
Deep Q-Learning optimizes market making by balancing price risk and spread profits.
problem Optimizing liquidity provision in financial markets.
method Reinforcement Learning applied to a market making problem with a reward function.
result Deep Q-Learning algorithms can recover the optimal market making strategy.
Study on decision-making cascades with agents having varying beliefs and noise levels.
problem Optimizing decision-making in a cascade of agents with heterogeneous beliefs and noise.
method Recursive belief update and analysis of optimal decision rules, predecessor selection problem characterization.
result Optimal decisions can deviate from true prior beliefs in certain conditions, highlighting the importance of social learning.
We propose a new model of minority game with so-called smart agents such that the standard deviation and the total loss in this model reach the theoretical minimum values in the limit of long time. The smart agents use trail and error method to make a choice but bring global optimization to the system, which suggests t…
ARCO-BO optimizes multi-agent design under heterogeneity, improving efficiency and performance.
problem Heterogeneous multi-agent optimization challenges in resource use and information sharing.
method ARCO-BO integrates a consensus mechanism, budget-aware sampling, and partial input sharing for heterogeneous design spaces.
result ARCO-BO outperforms independent and collaborative BO methods in complex multi-agent settings.
A new algorithm speeds up multi-agent reinforcement learning.
problem Complex interactions between agents in multi-agent reinforcement learning.
method Double averaging scheme for decentralized convex-concave saddle-point problems.
result The algorithm converges to the optimal solution at a global geometric rate.
Distributed algorithm converges to global optima with Gaussian noise.
problem Nonconvex optimization in network-based multi-agent settings.
method Consensus+innovations type algorithm with annealing-type approach and Gaussian noise.
result Convergence to the set of global optima of the sum function.
A method to minimize regret in multi-agent control systems with adversarial disturbances.
problem Optimal control of dynamical systems with adversarial disturbances and multiple agents.
method Reduction from online convex optimization to a distributed algorithm for multi-agent control.
result The resulting distributed algorithm has low regret relative to the optimal precomputed joint policy.
Develops a framework for analyzing multi-agent and many-body systems with feedback loops.
problem Optimal order of multi-agent and general many-body systems
method Derive macroscopic properties and optimal degree of order
result Optimal degree of order balances productivity, stability, and adaptability
An agent explores indefinitely in an environment with unlimited rewards.
problem Balancing exploration and exploitation in environments with unlimited rewards.
method Simple example of an environment with unbounded rewards and optimal agent behavior.
result An optimal agent always explores to maximize rewards, regardless of accumulated knowledge.
ATLAS uses LLMs to adaptively trade by optimizing prompts and coordinating agents.
problem Adapting LLMs for real-time financial decision-making in noisy markets.
method ATLAS integrates structured market data, uses Adaptive-OPRO for prompt optimization, and employs multi-agent coordination.
result Adaptive-OPRO consistently outperforms fixed prompts in financial trading.
This paper improves ADMM for decentralized learning with unreliable agents.
problem Improving convergence of ADMM in decentralized learning with unreliable agents.
method Rigorously analyzes ADMM convergence, provides network design guidelines, and proposes a robust variant.
result ADMM converges linearly to a neighborhood of the optimal solution under certain conditions.
A bandit framework selects the best reinforcement learning agent for costly interactions.
problem Optimal reinforcement learning agent selection in environments without simulators.
method Multi-arm bandit framework with surrogate rewards to select and learn from reinforcement learning agents.
result The framework consistently selects the optimal agent with more cumulative reward.
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 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.
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.
RL agents optimize order execution in a realistic market simulation.
problem Optimal order execution challenges in a complex market.
method Multi-agent RL in a historical order book simulation.
result RL agents converge to TWAP strategies in some scenarios.
Paper tackles RL for power grid topology optimization.
problem Managing large action spaces in growing power networks.
method Hierarchical multi-agent reinforcement learning (MARL) framework.
result MARL framework outperforms single-agent RL methods.
Pareto optimal centralized risk sharing with multiple agents
problem Centralized risk sharing with endogenous prices
method Inclusive and fair Pareto optimality
result Equivalence between inclusive and fair Pareto optimality and balanced sequential optimization
Optimal risk sharing found for heterogeneous risk attitudes using distortion risk measures.
problem Risk sharing in economies with diverse risk attitudes.
method Modeling preferences with distortion risk measures, using comonotonic and counter-monotonic principles.
result Optimal risk sharing strategies identified based on risk attitudes, reducing the n n n -agent problem to a two-agent formulation. A paper on optimizing ad bidding with multi-agent reinforcement learning.
problem Optimizing ad revenue and ROI in real-time display advertising.
method Multi-agent reinforcement learning with clustering and coordinated bidding.
result Cluster-based bidding outperforms single-agent and bandit approaches.
A model predicts financial agent behavior from rational to myopic decisions.
problem Modeling financial agent behavior from rational to myopic decisions.
method Microscopic model with Nash equilibrium and model predictive control.
result Derives macroscopic portfolio model replicating market dynamics.
Paper tackles non-convex optimization over networks with limited information.
problem Optimizing non-convex functions over a multi-agent network with zeroth-order information.
method Developed efficient distributed algorithms for different network topologies, analyzed convergence and rate of convergence.
result Proved convergence and rate of convergence for the set of stationary solutions.
Enhances financial analysis with multi-agent collaboration.
problem Limited use of AI-agent collaboration in financial research.
method Proposes a multi-agent system for financial investment research.
result Multi-agent system outperforms single-agent models.
Study optimal trading strategies with differing views and market prices.
problem Maximizing portfolio value with subjective asset value vs market price.
method Mean-field game approach to analyze interactions among agents with differing signals.
result Cross-sectional distribution of agents' inventories and price distribution dependence on shared information.
Paper addresses Byzantine attacks in decentralized optimization over networks.
problem Byzantine attacks in decentralized stochastic optimization over static and time-varying networks.
method Formulate a TV norm-penalized approximation of the problem, solve using stochastic subgradient method.
result Proposed method reaches a neighborhood of the Byzantine-free optimal solution.
BayGo framework optimizes decentralized learning in multi-agent networks.
problem Information heterogeneity in multi-agent networks.
method Bayesian learning and graph optimization framework with fast convergence.
result Estimation error decreases exponentially with each iteration.
Paper proposes a new algorithm to learn intrinsic rewards for policy-gradient agents.
problem Learning intrinsic rewards for policy-gradient agents is an open problem.
method Derives a novel algorithm for learning intrinsic rewards for policy-gradient based learning agents.
result Improved performance on most domains but not all when using the new algorithm.
Study optimal investment in large populations of competitive, heterogeneous agents.
problem Maximizing utility in a large, interacting agent system with relative performance concerns.
method Analyzes stochastic utility maximization game in finite and infinite agent settings, using graphon models and backward stochastic differential equations.
result Convergence of Nash equilibria and optimal utilities from finite to infinite agent models under specific conditions.
New methods improve multi-agent reinforcement learning by addressing rotational dynamics.
problem Reproducibility crisis in multi-agent reinforcement learning.
method Reframing MARL approaches using Variational Inequalities (VIs) and proposing gradient-based VI methods.
result Significant performance improvements across benchmarks, including better convergence to equilibrium strategies in zero-sum games.
New algorithm reduces individual regret and communication costs in cooperative bandits.
problem Optimal individual and group regret in cooperative multi-agent bandits.
method Integrates a new communication policy into a learning algorithm.
result Achieves optimal individual regret and constant communication costs.
Study optimal investment decisions for diverse risk-tolerant agents.
problem Optimizing investment choices for agents with varying risk preferences.
method Characterizes optimal behavior using certainty equivalents and lognormal risks.
result Derives optimal decision menus under known and uncertain preference distributions.