reinforcement learning
19 mentions across all digests
Reinforcement learning is a machine learning paradigm where agents learn through reward signals, applied in research contexts including fluid dynamics flow control, aircraft fault-tolerant control via hypernetworks, and black-box document retrieval optimization.
DeepMind’s David Silver just raised $1.1B to build an AI that learns without human data
David Silver launches Ineffable Intelligence with $1.1B to build artificial general intelligence via pure self-play learning, scaling the AlphaZero approach beyond human-labeled data constraints.
The Man Behind AlphaGo Thinks AI Is Taking the Wrong Path
AlphaGo's David Silver launches Ineffable Intelligence with $1.1B to build superintelligence via self-learning reinforcement systems, rejecting the industry's LLM scaling consensus as inefficient "fossil fuel" AI.
On Tackling Complex Tasks with Reward Machines and Signal Temporal Logics
Researchers boost reinforcement learning efficiency by replacing traditional rewards with Signal Temporal Logic formulas, enabling clearer formal specifications for complex control tasks.
Belief-State RWKV for Reinforcement Learning under Partial Observability
RWKV recurrent architecture applied to reinforcement learning under partial observability, letting agents infer hidden state from incomplete observations—addressing a core real-world RL constraint.
Enhancing sample efficiency in reinforcement-learning-based flow control: replacing the critic with an adaptive reduced-order model
Researchers replace neural critics with adaptive reduced-order models in reinforcement learning for fluid dynamics, dramatically cutting training data needs and computational cost.