11,188 research outputs found
Reward prediction error and declarative memory
Learning based on reward prediction error (RPE) was originally proposed in the context of nondeclarative memory. We postulate that RPE may support declarative memory as well. Indeed, recent years have witnessed a number of independent empirical studies reporting effects of RPE on declarative memory. We provide a brief overview of these studies, identify emerging patterns, and discuss open issues such as the role of signed versus unsigned RPEs in declarative learning
Flow for Meta Control
The psychological state of flow has been linked to optimizing human
performance. A key condition of flow emergence is a match between the human
abilities and complexity of the task. We propose a simple computational model
of flow for Artificial Intelligence (AI) agents. The model factors the standard
agent-environment state into a self-reflective set of the agent's abilities and
a socially learned set of the environmental complexity. Maximizing the flow
serves as a meta control for the agent. We show how to apply the meta-control
policy to a broad class of AI control policies and illustrate our approach with
a specific implementation. Results in a synthetic testbed are promising and
open interesting directions for future work
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