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Found 5 papers, 1 papers with code
Reward Design with Language Models
During training, the LLM evaluates an RL agent's behavior against the desired behavior described by the prompt and outputs a corresponding reward signal.
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.
Quasi-Equivalence Discovery for Zero-Shot Emergent Communication
In contrast, in this work, we present a novel problem setting and the Quasi-Equivalence Discovery (QED) algorithm that allows for zero-shot coordination (ZSC), i. e., discovering protocols that can generalize to independently trained agents.
Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations
Indeed, emergent communication is now a vibrant field of research, with common settings involving discrete cheap-talk channels.
Active Learning within Constrained Environments through Imitation of an Expert Questioner
Active learning agents typically employ a query selection algorithm which solely considers the agent's learning objectives.
