Adaptive replacements for domain-specific components in deep RL agents can yield better learning on new tasks without additional tuning.
Innateness, AlphaZero, and Artificial Intelligence
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
The concept of innateness is rarely discussed in the context of artificial intelligence. When it is discussed, or hinted at, it is often the context of trying to reduce the amount of innate machinery in a given system. In this paper, I consider as a test case a recent series of papers by Silver et al (Silver et al., 2017a) on AlphaGo and its successors that have been presented as an argument that a "even in the most challenging of domains: it is possible to train to superhuman level, without human examples or guidance", "starting tabula rasa." I argue that these claims are overstated, for multiple reasons. I close by arguing that artificial intelligence needs greater attention to innateness, and I point to some proposals about what that innateness might look like.
fields
cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
On Inductive Biases in Deep Reinforcement Learning
Adaptive replacements for domain-specific components in deep RL agents can yield better learning on new tasks without additional tuning.