Cooperation is often implicitly assumed when learning from other agents. Cooperation implies that the agent selecting the data, and the agent learning from the data, have the same goal, that the learner infer the intended hypothesis. Recent models in human and machine learning have demonstrated the possibility of coope…
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48 results for “Cooperative Learning”
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.
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.
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.
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…
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.
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…
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.
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 …
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…
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…
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.
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.
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.
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.
Efficient driving in urban traffic scenarios requires foresight. The observation of other traffic participants and the inference of their possible next actions depending on the own action is considered cooperative prediction and planning. Humans are well equipped with the capability to predict the actions of multiple i…
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 benchmarks MARL algorithms in cooperative tasks.
problem Lack of evaluation tasks and criteria for comparing MARL algorithms.
method Systematic evaluation of three MARL algorithm classes in diverse cooperative tasks.
result Insights into the effectiveness of different learning approaches.
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.
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.
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.
Adversaries can manipulate cooperative MARL networks.
problem Fragility of consensus-based MARL algorithms to adversarial attacks.
method Study of adversarial attacks on consensus-based MARL networks.
result Consensus-based MARL algorithms are vulnerable to adversarial manipulation.
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…
Introduces LTQL for factored policies in cooperative MARL.
problem Learning optimal joint policies in collaborative MARL scenarios.
method Logical Team Q-learning (LTQL) as a stochastic approximation to dynamic programming.
result LTQL provides factored policies for optimal joint behavior in cooperative MARL.
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.
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.
Distributed processing over networks relies on in-network processing and cooperation among neighboring agents. Cooperation is beneficial when agents share a common objective. However, in many applications agents may belong to different clusters that pursue different objectives. Then, indiscriminate cooperation will lea…
Hierarchies of temporally decoupled policies present a promising approach for enabling structured exploration in complex long-term planning problems. To fully achieve this approach an end-to-end training paradigm is needed. However, training these multi-level policies has had limited success due to challenges arising f…
Human players in professional team sports achieve high level coordination by dynamically choosing complementary skills and executing primitive actions to perform these skills. As a step toward creating intelligent agents with this capability for fully cooperative multi-agent settings, we propose a two-level hierarchica…
A scalable MARL algorithm using local rewards for cooperative multi-agent learning.
problem Scalability issues in cooperative multi-agent reinforcement learning due to large state and action spaces.
method LOMAQ algorithm incorporating local rewards in centralized training and decentralized execution.
result LOMAQ scales well compared to other methods, improving performance and convergence speed.
This paper studies the cooperative training of two generative models for image modeling and synthesis. Both models are parametrized by convolutional neural networks (ConvNets). The first model is a deep energy-based model, whose energy function is defined by a bottom-up ConvNet, which maps the observed image to the ene…
Learning when to communicate and doing that effectively is essential in multi-agent tasks. Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks. In this paper, we present Individualized Controlled Co…
LICA learns credit assignment for cooperative agents without explicit formulation.
problem Credit assignment among cooperative agents.
method Centralized critic as hypernetwork, adaptive entropy regularization.
result Significantly outperforms previous methods on benchmarks.
Flexible decentralized MARL framework for cooperative multi-agent learning.
problem Complexity and impracticality of centralized MARL in complicated applications.
method Flexible fully-decentralized actor-critic MARL framework using primal-dual hybrid gradient descent.
result Competitive performance in large-scale cooperative multi-agent environments.
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.
Attackers can significantly reduce team rewards in cooperative multi-agent reinforcement learning.
problem Robustness of cooperative multi-agent reinforcement learning to adversaries.
method Novel attack method involving training a policy network and using targeted adversarial examples.
result Reduces team reward from 20 to 9.4 by attacking a single agent, reducing winning rate from 98.9% to 0%.
We present a new model-based reinforcement learning algorithm, Cooperative Prioritized Sweeping, for efficient learning in multi-agent Markov decision processes. The algorithm allows for sample-efficient learning on large problems by exploiting a factorization to approximate the value function. Our approach only requir…
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.
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…
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.
Improved exploration in cooperative multi-agent reinforcement learning.
problem Limited expressiveness of Gaussian policies in DecSPG hinders effective exploration.
method Proposes decentralized diffusion policy learning (DDPL) with denoising diffusion probabilistic models.
result Consistently improved performance on various MARL benchmarks.
Unified framework for randomized exploration in cooperative MARL.
problem Efficient exploration in cooperative multi-agent reinforcement learning.
method Unified algorithm framework with two Thompson Sampling algorithms, CoopTS-PHE and CoopTS-LMC.
result Theoretical regret bound for parallel MDPs with linear transition.
Agents learn to communicate and solve navigation tasks efficiently.
problem Understanding and developing communication in artificial agents.
method Multi-agent navigation tasks in gridworld environments.
result Agents develop an interpretable communication protocol that optimally solves tasks.
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…
New method combines value function decomposition and policy gradients for cooperative multi-agent reinforcement learning.
problem Challenges in cooperative multi-agent reinforcement learning, especially credit assignment and large action spaces.
method Decomposed Soft Actor-Critic (mSAC) method with Q network architecture, discrete probabilistic policy, and counterfactual advantage function.
result Significantly outperforms policy-based approach COMA and achieves competitive results with SOTA value-based approach Qmix.
Semi-supervised learning methods are usually employed in the classification of data sets where only a small subset of the data items is labeled. In these scenarios, label noise is a crucial issue, since the noise may easily spread to a large portion or even the entire data set, leading to major degradation in classific…
Learning to cooperate with friends and compete with foes is a key component of multi-agent reinforcement learning. Typically to do so, one requires access to either a model of or interaction with the other agent(s). Here we show how to learn effective strategies for cooperation and competition in an asymmetric informat…
Truthful Self-Playstat.ML
Framework improves self-play for cooperative multi-agent learning.
problem Evolutionary learning converges to bad local optima in multi-agent RL.
method Add imaginary rewards using peer prediction method to elicit truthful signals.
result State-of-the-art performance in predator prey, traffic junction and StarCraft tasks.