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Found 4 papers, 4 papers with code
In-Context Reinforcement Learning for Variable Action Spaces
Recently, it has been shown that transformers pre-trained on diverse datasets with multi-episode contexts can generalize to new reinforcement learning tasks in-context.
Emergence of In-Context Reinforcement Learning from Noise Distillation
Recently, extensive studies in Reinforcement Learning have been carried out on the ability of transformers to adapt in-context to various environments and tasks.
XLand-MiniGrid: Scalable Meta-Reinforcement Learning Environments in JAX
Inspired by the diversity and depth of XLand and the simplicity and minimalism of MiniGrid, we present XLand-MiniGrid, a suite of tools and grid-world environments for meta-reinforcement learning research.
Mediated Multi-Agent Reinforcement Learning
The majority of Multi-Agent Reinforcement Learning (MARL) literature equates the cooperation of self-interested agents in mixed environments to the problem of social welfare maximization, allowing agents to arbitrarily share rewards and private information.
