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Found 3 papers, 3 papers with code
Better Safe than Sorry: Evidence Accumulation Allows for Safe Reinforcement Learning
The agent makes no decision by default, and the burden of proof to make a decision falls on the policy to accrue evidence strongly in favor of a single decision.
Combining imagination and heuristics to learn strategies that generalize
Deep reinforcement learning can match or exceed human performance in stable contexts, but with minor changes to the environment artificial networks, unlike humans, often cannot adapt.
Learning model-based strategies in simple environments with hierarchical q-networks
Recent advances in deep learning have allowed artificial agents to rival human-level performance on a wide range of complex tasks; however, the ability of these networks to learn generalizable strategies remains a pressing challenge.
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