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Found 35 papers, 13 papers with code
Multi-Agent Risks from Advanced AI
The rapid development of advanced AI agents and the imminent deployment of many instances of these agents will give rise to multi-agent systems of unprecedented complexity.
Neural Contextual Reinforcement Framework for Logical Structure Language Generation
The Neural Contextual Reinforcement Framework introduces an innovative approach to enhancing the logical coherence and structural consistency of text generated by large language models.
Cultural Evolution of Cooperation among LLM Agents
Further, Claude 3. 5 Sonnet can make use of an additional mechanism for costly punishment to achieve yet higher scores, while Gemini 1. 5 Flash and GPT-4o fail to do so.
Open-Endedness is Essential for Artificial Superhuman Intelligence
In this position paper, we argue that the ingredients are now in place to achieve openendedness in AI systems with respect to a human observer.
Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning
Cultural accumulation drives the open-ended and diverse progress in capabilities spanning human history.
Genie: Generative Interactive Environments
We introduce Genie, the first generative interactive environment trained in an unsupervised manner from unlabelled Internet videos.
Towards a Pretrained Model for Restless Bandits via Multi-arm Generalization
Restless multi-arm bandits (RMABs), a class of resource allocation problems with broad application in areas such as healthcare, online advertising, and anti-poaching, have recently been studied from a multi-agent reinforcement learning perspective.
Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas
In social psychology, Social Value Orientation (SVO) describes an individual's propensity to allocate resources between themself and others.
Human-Timescale Adaptation in an Open-Ended Task Space
Foundation models have shown impressive adaptation and scalability in supervised and self-supervised learning problems, but so far these successes have not fully translated to reinforcement learning (RL).
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.
Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning
Agents learn to produce resources in a spatially complex world, trade them with one another, and consume those that they prefer.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Learning Robust Real-Time Cultural Transmission without Human Data
We provide a method for generating zero-shot, high recall cultural transmission in artificially intelligent agents.
Collaborating with Humans without Human Data
Here, we study the problem of how to train agents that collaborate well with human partners without using human data.
A multi-agent reinforcement learning model of reputation and cooperation in human groups
Collective action demands that individuals efficiently coordinate how much, where, and when to cooperate.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Modelling Cooperation in Network Games with Spatio-Temporal Complexity
Such systems have local incentives for individuals, whose behavior has an impact on the global outcome for the group.
Neural Recursive Belief States in Multi-Agent Reinforcement Learning
We also show that higher-order belief models outperform agents with lower-order models.
Open Problems in Cooperative AI
We see opportunity to more explicitly focus on the problem of cooperation, to construct unified theory and vocabulary, and to build bridges with adjacent communities working on cooperation, including in the natural, social, and behavioural sciences.
Negotiating Team Formation Using Deep Reinforcement Learning
When autonomous agents interact in the same environment, they must often cooperate to achieve their goals.
Learning to Incentivize Other Learning Agents
The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years.
Learning to Resolve Alliance Dilemmas in Many-Player Zero-Sum Games
Here we argue that a systematic study of many-player zero-sum games is a crucial element of artificial intelligence research.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Social diversity and social preferences in mixed-motive reinforcement learning
Recent research on reinforcement learning in pure-conflict and pure-common interest games has emphasized the importance of population heterogeneity.
Smooth markets: A basic mechanism for organizing gradient-based learners
With the success of modern machine learning, it is becoming increasingly important to understand and control how learning algorithms interact.
A Generalized Training Approach for Multiagent Learning
This paper investigates a population-based training regime based on game-theoretic principles called Policy-Spaced Response Oracles (PSRO).
OpenSpiel: A Framework for Reinforcement Learning in Games
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
Intrinsic Social Motivation via Causal Influence in Multi-Agent RL
Therefore, we also employ influence to train agents to use an explicit communication channel, and find that it leads to more effective communication and higher collective reward.
Learning Reciprocity in Complex Sequential Social Dilemmas
We analyse the resulting policies to show that the reciprocating agents are strongly influenced by their co-players' behavior.
Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research
Evolution has produced a multi-scale mosaic of interacting adaptive units.
The Hanabi Challenge: A New Frontier for AI Research
From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making.
Causal Reasoning from Meta-reinforcement Learning
Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents.
Malthusian Reinforcement Learning
Here we explore a new algorithmic framework for multi-agent reinforcement learning, called Malthusian reinforcement learning, which extends self-play to include fitness-linked population size dynamics that drive ongoing innovation.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Evolving intrinsic motivations for altruistic behavior
Multi-agent cooperation is an important feature of the natural world.
Multiagent Systems
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
We present the Bayesian action decoder (BAD), a new multi-agent learning method that uses an approximate Bayesian update to obtain a public belief that conditions on the actions taken by all agents in the environment.
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions.
Learning to Understand Goal Specifications by Modelling Reward
Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards.
Inequity aversion improves cooperation in intertemporal social dilemmas
Groups of humans are often able to find ways to cooperate with one another in complex, temporally extended social dilemmas.
