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Found 11 papers, 5 papers with code
Scaling Instructable Agents Across Many Simulated Worlds
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI.
CogBench: a large language model walks into a psychology lab
Large language models (LLMs) have significantly advanced the field of artificial intelligence.
Meta-Learned Models of Cognition
Meta-learning is a framework for learning learning algorithms through repeated interactions with an environment as opposed to designing them by hand.
Machine Psychology
Large language models (LLMs) show increasingly advanced emergent capabilities and are being incorporated across various societal domains.
Data Distributional Properties Drive Emergent In-Context Learning in Transformers
In further experiments, we found that naturalistic data distributions were only able to elicit in-context learning in transformers, and not in recurrent models.
Semantic Exploration from Language Abstractions and Pretrained Representations
We show that these pretrained representations drive meaningful, task-relevant exploration and improve performance on 3D simulated environments.
Can language models learn from explanations in context?
In summary, explanations can support the in-context learning of large LMs on challenging tasks.
Tell me why! Explanations support learning relational and causal structure
Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents.
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agents
There has been rapidly growing interest in meta-learning as a method for increasing the flexibility and sample efficiency of reinforcement learning.
Meta-learning in natural and artificial intelligence
Meta-learning, or learning to learn, has gained renewed interest in recent years within the artificial intelligence community.
Temporal Difference Uncertainties as a Signal for Exploration
Instead, we incorporate it as an intrinsic reward and treat exploration as a separate learning problem, induced by the agent's temporal difference uncertainties.
