I make the case why people iteratively training any model should learn some core concerns of reinforcement learning.
Choices, risks, and reward reporting. Recommendations for how to integrate RL systems with society.
A tour of control theory, multi-agent RL, and hierarchical learning.
Industry labs power up their robot learning research with parallelization!
How RL is starting to be used by industry and how RL is heading to a framing more suited for industrial scales.
Multi-agent scenarios make reward maximization a risk. Discussing when, rather than if, we should believe in the Reward Hypothesis.
How simulator exploitation, a dual of over-optimization, in AI is the canary in the coal mine for what negative implications could come from…
I don’t even like the idea of flying delivery drones, but that will be all we have.
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Democratizing Automation