A review of cooperation in multi-agent learning
Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous
disciplines, including game theory, economics, social sciences, and evolutionary biology …
disciplines, including game theory, economics, social sciences, and evolutionary biology …
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 …
supervised learning problems, but so far these successes have not fully translated to …
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 …
supervised learning problems, but so far these successes have not fully translated to …
Foundational challenges in assuring alignment and safety of large language models
This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …
language models (LLMs). These challenges are organized into three different categories …
Influencing towards stable multi-agent interactions
Learning in multi-agent environments is difficult due to the non-stationarity introduced by an
opponent's or partner's changing behaviors. Instead of reactively adapting to the other …
opponent's or partner's changing behaviors. Instead of reactively adapting to the other …
Get it in writing: Formal contracts mitigate social dilemmas in multi-agent rl
PJK Christoffersen, AA Haupt… - arXiv preprint arXiv …, 2022 - arxiv.org
Multi-agent reinforcement learning (MARL) is a powerful tool for training automated systems
acting independently in a common environment. However, it can lead to sub-optimal …
acting independently in a common environment. However, it can lead to sub-optimal …
Influencing long-term behavior in multiagent reinforcement learning
The main challenge of multiagent reinforcement learning is the difficulty of learning useful
policies in the presence of other simultaneously learning agents whose changing behaviors …
policies in the presence of other simultaneously learning agents whose changing behaviors …
Information design in multi-agent reinforcement learning
Reinforcement learning (RL) is inspired by the way human infants and animals learn from
the environment. The setting is somewhat idealized because, in actual tasks, other agents in …
the environment. The setting is somewhat idealized because, in actual tasks, other agents in …
Adaptive incentive design with multi-agent meta-gradient reinforcement learning
Critical sectors of human society are progressing toward the adoption of powerful artificial
intelligence (AI) agents, which are trained individually on behalf of self-interested principals …
intelligence (AI) agents, which are trained individually on behalf of self-interested principals …
The good shepherd: An oracle agent for mechanism design
From social networks to traffic routing, artificial learning agents are playing a central role in
modern institutions. We must therefore understand how to leverage these systems to foster …
modern institutions. We must therefore understand how to leverage these systems to foster …