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.
Paper develops a private algorithm for multi-agent learning in bandits.
problem Private cooperative learning in decentralized systems.
method Developed extsc{FedUCB} algorithm for multi-agent learning.
result Improves pseudoregret bounds and empirical performance.
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 Θ ( T ) Θ(T) Θ ( T ) to O ( log T ) O(\log T) O ( log T ) messages. result Achieves state-of-the-art performance with significantly reduced communication cost.
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.
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…
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.
Algorithm reduces regret in multi-player bandits with unknown collision rewards.
problem Reducing regret in multi-player multi-armed bandits with unknown collision rewards.
method Proposes an algorithm that combines a modified successive elimination strategy with a communication protocol to estimate suboptimality gaps and coordinate among players.
result Achieves logarithmic regret for the problem when collision reward is unknown.
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 O ( γ N log ( T ) [ K T + D K ] ) O(γN\log(T)[\sqrt{KT} + DK]) O ( γ N log ( T ) [ K T + D K ]) . 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.
Study on individual regret in cooperative MAB with agents communicating over a graph.
problem Individual regret in cooperative stochastic multi-armed bandits with communication constraints.
method Analyzed COOP-SE algorithm, derived individual regret bounds under various communication constraints.
result First to show an individual regret bound in cooperative stochastic MAB independent of graph diameter.
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.
New strategy achieves optimal regret without communication or collisions in multi-player bandit.
problem Cooperative multi-player stochastic multi-armed bandit with shared randomness.
method Combination of combinatorial approach to generalize geometric intuition.
result Achieves near-optimal regret i l d e O ( T ) ilde{O}(\sqrt{T}) i l d e O ( T ) for any number of players and arms without collisions. 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.
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.
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.
Coop-FTPL algorithm minimizes network regret in semi-bandit settings.
problem Online combinatorial optimization with semi-bandit feedback on a network of agents.
method Cooperative Follow The Perturbed Leader (Coop-FTPL) algorithm with new loss estimation procedure.
result Expected regret of Coop-FTPL is of order Q mkT log(k)(kα1 /Q + m), with a state-of-the-art computational complexity of T^3/2.
Two non-communicating players minimize regret in a multi-armed bandit game.
problem Optimal regret in non-communicating multi-armed bandit players.
method Proposed a strategy with no collisions, achieving near-optimal regret.
result Near-optimal regret of O ( T log ( T ) ) O(\sqrt{T \log(T)}) O ( T log ( T ) ) with very high probability. 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. 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 O ( K N M log T + K M T log T ) \mathcal{O}(KNM\log T + K\sqrt{MT\log T}) O ( K N M log T + K M T log T ) . Algorithm optimizes multi-player learning with noisy rewards without direct communication.
problem Cooperative multi-player learning with noisy rewards and no communication.
method Upper and lower confidence bounds algorithm for optimal action selection.
result Achieves logarithmic O ( log T Δ a ) O(\frac{\log T}{Δ_{\bm{a}}}) O ( Δ a l o g T ) and O ( T log T ) O(\sqrt{T\log T}) O ( T log T ) regret. 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.
We study a decentralized cooperative stochastic multi-armed bandit problem with K K K arms on a network of N N N agents. In our model, the reward distribution of each arm is the same for each agent and rewards are drawn independently across agents and time steps. In each round, each agent chooses an arm to play and subsequ…
A simple algorithm reduces federated contextual linear bandits' regret efficiently.
problem Solving federated contextual linear bandits with asynchronous agents.
method Proposed a simple algorithm exttt{FedLinUCB} based on optimism principle.
result Proved exttt{FedLinUCB} has bounded regret i l d e O ( d ∑ m = 1 M T m ) ilde{O}(d\sqrt{\sum_{m=1}^M T_m}) i l d e O ( d ∑ m = 1 M T m ) and communication complexity i l d e O ( d M 2 ) ilde{O}(dM^2) i l d e O ( d M 2 ) . We consider the non-stochastic version of the (cooperative) multi-player multi-armed bandit problem. The model assumes no communication at all between the players, and furthermore when two (or more) players select the same action this results in a maximal loss. We prove the first T \sqrt{T} T -type regret guarantee for th…
A new algorithm RESYNC for defenders against malicious attackers in multi-player bandits.
problem Malicious players colliding with cooperative players to prevent rewards.
method Decentralized and robust algorithm RESYNC for defenders.
result RESYNC algorithm is order-optimal, performing gracefully as the number of collisions increases.
In this paper we propose a novel framework for decentralized, online learning by many learners. At each moment of time, an instance characterized by a certain context may arrive to each learner; based on the context, the learner can select one of its own actions (which gives a reward and provides information) or reques…
This work improves privacy in federated combinatorial bandits by balancing regret and privacy.
problem Privacy-preserving learning in competitive online learning settings with quality constraints.
method Proposes P-FCB algorithm for federated combinatorial bandits, balancing regret and privacy.
result Improves regret while maintaining quality constraints and privacy guarantees.
A distributed algorithm reduces communication cost in linear bandits to near-optimal levels.
problem Cooperative linear bandit optimization with stochastic contexts.
method DisBE-LUCB algorithm, DecBE-LUCB algorithm, sharing information through a central server or immediate neighbors.
result Communication cost of DisBE-LUCB matches information-theoretic lower bound up to logarithmic factors.
Unified framework controls false discovery rate in bandit multiple testing.
problem Designing adaptive algorithms to identify true discoveries in multiple hypothesis testing.
method Unified modular framework using e-processes for FDR control in arbitrary settings.
result Unified framework ensures FDR control for dependent and simultaneous arm queries.
Develops methods for cooperative Bayesian inference.
problem Cooperation between learning agents.
method Sequential Bayesian inference approaches.
result Theoretical foundation for cooperative inference.
Bayesian algorithms minimize cumulative regret in decentralized multi-agent bandits.
problem Minimizing cumulative regret in a decentralized multi-agent multi-armed bandit problem.
method Proposed decentralized Bayesian multi-armed bandit framework, including Thompson Sampling and Bayes-UCB algorithms.
result Regret scales logarithmically with constants matching those of an optimal centralized agent.
A decentralized algorithm minimizes cumulative regret in stochastic linear bandits with safety constraints.
problem Efficiently solving a linear bandit-optimization problem over a network of agents with safety constraints.
method DLUCB: a fully decentralized algorithm that minimizes cumulative regret through UCB strategy and consensus procedure.
result Near-optimal regret performance of O ( d log N T N T ) \mathcal{O}(d\log{NT}\sqrt{NT}) O ( d log N T N T ) with O ( d N 2 ) \mathcal{O}(dN^2) O ( d N 2 ) communication rate. 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…
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.
Partner-aware algorithms improve AI collaboration in multi-agent settings.
problem Improving AI cooperation in teams with shared rewards.
method Proposed Partner-Aware strategy extending Upper Confidence Bound for decentralized MAB.
result Achieves logarithmic regret in collaborative decision-making.
New algorithm for multi-player bandits with selfish players, achieving logarithmic regret.
problem Challenges of robustness to selfish players in multi-player bandits.
method First algorithm robust to selfish players achieving logarithmic regret, with or without collision observation.
result Achieved logarithmic regret for robust algorithms to selfish players in multi-player bandits.
No communication allows optimal instance-dependent regret guarantees in multi-player bandits.
problem Achieving optimal instance-dependent regret in multi-player multi-armed bandits without communication.
method Characterization of Pareto optimal trade-offs and development of an algorithm.
result Achieving optimal instance-dependent regret requires strict sub-optimality in other regimes.
We study agents communicating over an underlying network by exchanging messages, in order to optimize their individual regret in a common nonstochastic multi-armed bandit problem. We derive regret minimization algorithms that guarantee for each agent v v v an individual expected regret of $\widetilde{O}\left(\sqrt{\left(…
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.
A novel approach tackles sparse linear bandits with reduced communication costs and minimal cumulative regret.
problem Sparse linear bandits with high-dimensional feature vectors and limited relevant features.
method Cooperative Thresholded Lasso using Lasso and ridge regression for dimension reduction and aggregation.
result Regret bound of O ( s 0 log d + s 0 T ) \mathcal{O}(s_0 \log d + s_0 \sqrt{T}) O ( s 0 log d + s 0 T ) with high probability. 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.
Federated learning for combinatorial multi-agent bandits reduces regret and speeds up with fewer communications.
problem Online combinatorial optimization with noisy feedback and cooperation.
method Transforms offline algorithms into online multi-agent algorithms with sublinear regret and communication efficiency.
result Achieves sublinear regret and linear speedup with more agents, communication-efficient.
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 …