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Found 7 papers, 2 papers with code
Amortized learning of neural causal representations
The CRN represent causal models using continuous representations and hence could scale much better with the number of variables.
Deep Reinforcement Learning and its Neuroscientific Implications
The emergence of powerful artificial intelligence is defining new research directions in neuroscience.
Meta-learning of Sequential Strategies
In this report we review memory-based meta-learning as a tool for building sample-efficient strategies that learn from past experience to adapt to any task within a target class.
Evolving intrinsic motivations for altruistic behavior
Multi-agent cooperation is an important feature of the natural world.
Multiagent Systems
Been There, Done That: Meta-Learning with Episodic Recall
Meta-learning agents excel at rapidly learning new tasks from open-ended task distributions; yet, they forget what they learn about each task as soon as the next begins.
Learning to reinforcement learn
We unpack these points in a series of seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL.
A high-reproducibility and high-accuracy method for automated topic classification
Much of human knowledge sits in large databases of unstructured text.
