Deep Reinforcement Learning

AI training method inspired by behaviorism, used for autonomous cars and drones; cited as bioinspired success

Neighborhood — ranked by edge-count

concept

Bioinspiration
implements
Use of biological principles to guide machine design; the paper calls for systematic rather than ad hoc bioinspiration

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Reinforcement Learningframework0.902
Alternative framework for agent behavior; based on reward maximization rather than free energy minimization.
Deep Learningconcept0.877
Learning hierarchical representations of non-decomposable functions; proposed as formal equivalent to ETI process.
Reinforcement learning (RL)concept0.838
Machine learning paradigm where agents learn to maximize cumulative reward through interaction.
Inverse Reinforcement Learningmethod0.835
Value learning method inferring reward function from expert demonstrations; reviewed as insufficient for superintelligent alignment
Reinforcement Learning from Human Feedbackmethod0.834
Method for fine-tuning LMs based on human preferences; mentioned as combining RL and LMs.
Reinforcement Learning for Tissuesmethod0.831
Proposed experimental paradigm to train morphogenesis using rewards and punishments, treating tissues as learning agents.
Reinforcement learning of tissuesconcept0.825
The hypothesis that cellular collectives can be trained via rewards/punishments to produce specific morphological outcomes.
Reinforcement Learning from AI Feedbackframework0.818
Variant of RLHF where human feedback is replaced with AI-generated feedback for harmlessness.