We study the explore-exploit tradeoff in distributed cooperative decision-making using the context of the multiarmed bandit (MAB) problem. For the distributed cooperative MAB problem, we design the cooperative UCB algorithm that comprises two interleaved distributed processes: (i) running consensus algorithms for estim…
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48 results for “cooperative decision-making”
Study how communication and feedback graphs affect learning outcomes.
problem Understanding the impact of feedback graphs on cooperative online learning.
method Analyzed network regret in terms of the independence number of the strong product of communication and feedback graphs.
result Proved bounds for network regret and demonstrated the non-improvable nature of positive results in pathological cases.
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.
The paper addresses private and Byzantine-proof cooperative decision-making in multi-agent systems.
problem Designing algorithms for multi-agent decision-making that are private and resilient to faulty agents.
method Upper-confidence bound algorithms for stochastic bandit problems under privacy and Byzantine conditions.
result Optimal regret achieved in both private and Byzantine-tolerant settings.
ComEx protocol reduces communication costs in cooperative bandits.
problem Minimizing communication costs in cooperative bandits while maintaining optimal performance.
method Developed ComEx protocol to reduce communication from to messages.
result Achieves state-of-the-art performance with significantly reduced communication cost.
Agents cooperate to make decisions in multi-armed bandits over a graph.
problem Optimizing decisions in multi-agent multi-armed bandits with shared information.
method Designs consensus-based distributed estimation and cooperative algorithms for group decision-making.
result Achieves group performance close to centralized fusion center.
This thesis analyzes MACL systems with low-regret learning algorithms for sequential decision making.
problem Designing efficient learning algorithms for multi-agent cooperative systems to minimize regret.
method Analyzes and develops algorithms for cooperative multi-agent multi-armed bandit problems and online convex optimization in distributed settings.
result Presented regret lower bounds and efficient algorithms for achieving these bounds, providing guidance on communication protocols.
Algorithm reduces decision-making errors in multi-agent bandit problems.
problem Minimizing decision errors in multi-agent multi-armed bandit problems.
method RBO-Coop-UCB algorithm with Bayesian change point detection.
result Expected group regret is upper bounded by .
Study cooperative bandit learning with imperfect communication, achieving near-optimal performance.
problem Real-world distributed decision-making with imperfect communication.
method Proposed decentralized algorithms for three communication scenarios: stochastic networks, random delays, and adversarially corrupted rewards.
result Achieved competitive performance and near-optimal guarantees on group regret.
A UCB algorithm reduces regret in cooperative multi-agent graph bandits.
problem Cooperative multi-agent decision-making on a graph with shared rewards.
method Upper Confidence Bound (UCB) algorithm for minimizing regret.
result The Multi-G-UCB algorithm achieves expected regret .
In order to drive safely and efficiently under merging scenarios, autonomous vehicles should be aware of their surroundings and make decisions by interacting with other road participants. Moreover, different strategies should be made when the autonomous vehicle is interacting with drivers having different level of coop…
Framework for games with uncertain parameters, ensuring no player can improve by changing strategy.
problem Non-cooperative games with globally uncertain parameters and no common prior.
method Mixed strategies and subjective priors, Extended Equilibrium defined by fixed-point argument.
result Existence of Extended Equilibrium under certain conditions.
Efficient Connected and Automated Driving System with Multi-agent Graph Reinforcement Learningstat.ML
This paper improves transportation efficiency by teaching automated vehicles to cooperate.
problem Improving efficiency and safety of transportation systems with automated vehicles.
method Multi-agent graph reinforcement learning with attention mechanism.
result Automated vehicles can achieve better performance when learning to cooperate with each other.
Develops methods for cooperative Bayesian inference.
problem Cooperation between learning agents.
method Sequential Bayesian inference approaches.
result Theoretical foundation for cooperative inference.
Multi-cell cooperative processing with limited backhaul traffic is studied for cellular uplinks. Aiming at reduced backhaul overhead, a sparsity-regularized multi-cell receive-filter design problem is formulated. Both unstructured distributed cooperation as well as clustered cooperation, in which base station groups ar…
Equilibrium pricing of securities in the co-presence of cooperative and non-cooperative populationsq-fin.MF
Develops an equilibrium model for securities pricing in a mixed cooperative and non-cooperative market.
problem Equilibrium pricing of securities in a market with cooperative and non-cooperative agents.
method Conditional extended mean-field control for cooperative agents, mean-field model for both cooperative and non-cooperative agents.
result Existence of a unique equilibrium for both finite-agent and mean-field models under certain conditions.
Statistical Gamesmath.ST
Unified framework for Bayesian and Frequentist statistics.
problem Embedding Bayesian statistics within a broader decision-making framework.
method Game theory and statistical analysis.
result Statistical games unify Bayesian and Frequentist statistics.
Stable cooperation emerges in fluctuating environments.
problem Evolutionary stability of cooperation in fluctuating conditions.
method Agents with fluctuating wealth share public goods, leading to cooperation.
result Agents with cooperation produce an advantage in fluctuating environments.
The paper tackles cooperative RL with function approximation, achieving near-optimal learning with limited communication.
problem Cooperative multi-agent reinforcement learning with function approximation.
method Careful message-passing and cooperative value iteration.
result Achieving near-optimal no-regret learning with limited communication in cooperative multi-agent settings.
Proposes a value-oriented forecast reconciliation method for renewables in electricity markets.
problem Forecast reconciliation overlooks the value of forecasts in decision-making, leading to unfair outcomes.
method Value-oriented forecast reconciliation using a Nash bargaining framework and a primal-dual algorithm for parameter estimation.
result Consistently increases profits for all agents involved in an aggregated wind energy trading problem.
The cooperative hierarchical structure is a common and significant data structure observed in, or adopted by, many research areas, such as: text mining (author-paper-word) and multi-label classification (label-instance-feature). Renowned Bayesian approaches for cooperative hierarchical structure modeling are mostly bas…
Proposes a method to cluster tasks for constructive cooperative multi-tasking.
problem Destructive cooperation in cooperative multi-tasking.
method Semantic clustering followed by end-to-end joint training within clusters.
result Effective mitigation of destructive cooperation and negative transfer.
CSAC enables cooperative reinforcement learning for multi-stage tasks.
problem Coordinating consecutive reinforcement learning agents for long-term multi-stage tasks.
method CSAC modifies each agent's policy to maximize both current and next agent's critic.
result CSAC outperforms uncooperative policies and single-agent training in multi-room maze domain.
Cooperation is a persistent behavioral pattern of entities pooling and sharing resources. Its ubiquity in nature poses a conundrum. Whenever two entities cooperate, one must willingly relinquish something of value to the other. Why is this apparent altruism favored in evolution? Classical solutions assume a net fitness…
Cooperative communication plays a central role in theories of human cognition, language, development, culture, and human-robot interaction. Prior models of cooperative communication are algorithmic in nature and do not shed light on why cooperation may yield effective belief transmission and what limitations may arise …
New algorithm reduces complexity in multi-agent reinforcement learning.
problem High computational complexity in exact computations for multi-agent reinforcement learning.
method Design of a scalable algorithm based on Natural Policy Gradient, using local information and limited communication.
result Converges to globally optimal policy with dimension-free complexity and localization error.
A generalized public goods game with coupling of individual ability and project benefitphysics.soc-ph
Facing a heavy task, any single person can only make a limited contribution and team cooperation is needed. As one enjoys the benefit of the public goods, the potential benefits of the project are not always maximized and may be partly wasted. By incorporating individual ability and project benefit into the original pu…
New method combines heuristics and search techniques to speed up cooperative planning for autonomous vehicles.
problem Efficient cooperative planning for autonomous vehicles in complex traffic scenarios.
method Combining learned heuristics with Monte Carlo Tree Search (MCTS) to guide search towards promising actions.
result Better solutions at lower computational costs achieved through accelerated planning.
Improved cooperation between levels boosts reinforcement learning performance.
problem Training multi-level policies in hierarchical reinforcement learning.
method Modeling policy optimization as a multi-agent process and inducing cooperation between sub-policies.
result Inducing cooperation between sub-policies leads to stronger and more sample-efficient policies.
We consider in a market model the cooperative emergence of value due to a positive feedback between perception of needs and demand. Here we consider also a negative feedback from production of the traded products, and find that this cooperativity is robust, provided that the production rate is slow. Cooperativity is fo…
A new algorithm reduces regret in cooperative multi-agent bandits with heavy-tailed data.
problem Cooperative multi-agent bandits with heavy-tailed data.
method MP-UCB algorithm incorporating robust estimation with message-passing protocol.
result Optimal regret bounds for MP-UCB in various settings.
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.
Cooperation information sharing is important to theories of human learning and has potential implications for machine learning. Prior work derived conditions for achieving optimal Cooperative Inference given strong, relatively restrictive assumptions. We relax these assumptions by demonstrating convergence for any disc…
Asynchronous cooperative learning rules ensure all agents converge to correct hypothesis.
problem Cooperative learning in networks with unreliable communication.
method Proposed robust cooperative learning rule for weak communication networks.
result All agents' beliefs exponentially decay to the correct hypothesis.
Study shows cooperation can improve everyone's market efficiency.
problem Understanding when collective cooperation improves market efficiency.
method General semimartingale framework, deriving necessary and sufficient conditions.
result Strict improvement in each agent's indirect utility when cooperation is beneficial.
A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success. This multi-goal multi-agent setting poses difficulties for recent algorithms, which primarily target settings with a single global reward, due to two new challenges: efficient explo…
Enhances cooperative multi-task SemCom for distributed users.
problem Performance degradation in cooperative multi-tasking due to negative information transfer.
method Federated learning (FL) with semantic-aware task clustering.
result Constructive cooperation across distributed users with semantic-aware task clustering.
Maker-taker fees can prevent algorithmic cooperation in market making, but not always.
problem Unexpected cooperation among independent algorithms in market making.
method Modeling market making as a repeated game, experimental analysis of transaction costs and rebates.
result Maker-taker fee models can destabilize cooperation, but not always with a specific relationship between costs and rebates.
A new method for combining multiple data views in supervised learning.
problem Combining multiple data views in supervised learning, especially in biology and medicine.
method Cooperative learning combines squared error loss with an agreement penalty to encourage predictions from different data views to agree.
result Cooperative learning achieves higher predictive accuracy on simulated and real multiomics data.
Analyzes gaming in federated learning systems and provides design principles.
problem Gaming and cooperation in federated learning systems under partial observability.
method Developed an analytical framework to separate welfare-improving behavior from metric gaming.
result Derived threshold conditions for deterring harmful gaming and preserving cooperation.
WILD-SCAV benchmarks AI in complex 3D FPS environments.
problem Lack of complexity and diversity in RL environments.
method Developed a 3D open-world FPS game environment.
result Demonstrates effectiveness in benchmarking RL algorithms.
Study shows market makers can cooperate without communication.
problem Concerns of collusion in AI-driven market-making.
method Formulated as a repeated game, studied with Q-learning.
result Market makers can learn cooperative strategies without communication.
New cooperative dynamics enhances retrieval performance in neural networks.
problem Understanding emergent computational capabilities in disordered systems.
method Leveraging statistical mechanics, extended neural network architecture for hetero-associative memory.
result Layers trained with less informative datasets develop retrieval regions of the same amplitude, leading to optimal performance.
Interpretable ML methods for better decision-making with explanations.
problem Lack of transparency in black-box ML models.
method Use of Formal Concept Analysis and cooperative game theory to assess attribute importance and reduce attribute count.
result Developed methods to assess attribute importance and reduce attribute count in ML models.
Learning to cooperate is crucially important in multi-agent environments. The key is to understand the mutual interplay between agents. However, multi-agent environments are highly dynamic, where agents keep moving and their neighbors change quickly. This makes it hard to learn abstract representations of mutual interp…
New algorithm reduces regret in collaborative multi-agent bandit problems.
problem Optimizing decisions in a network of agents with communication delays.
method Follow-the-Regularized-Leader (FTRL) algorithm with suitable regularizers and communication protocols.
result Upper bound on individual regret matches lower bound up to a constant factor.
Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensorscs.CV
Cooperative perception improves 3D object detection in autonomous vehicles.
problem Limited field-of-view and occlusion in single sensor data.
method Early fusion of point clouds from multiple sensors, late fusion of independently detected bounding boxes, and hybrid combination.
result Early fusion approach outperforms late fusion by significantly higher recall (95%) compared to single-point sensing (30%).
Paper explores alternative cooperative game theory methods for machine learning feature attribution.
problem Debate over Shapley values' relevance in feature attribution.
method Introduces Weber and Harsanyi sets as alternative allocation schemes.
result Provides a coherent framework for designing robust feature attributions.