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Reinforcement Learning (RL)

Definition
AI-generated

Reinforcement learning (RL) optimizes a policy that chooses actions in an environment to maximize expected cumulative reward, using value functions, policy gradients, or model-based planning; exploration–exploitation trade-offs and credit assignment over long horizons are central challenges.

Topics
LLM and Agents
Related
AI Agent Machine Learning Neural Network

Why it matters in GWAS

RL appears in adaptive sequential trial design, some active learning loops for variant curation, and agent-style LLM tool use; for genetic discovery, reward definitions must avoid gaming proxy endpoints or reinforcing biases in training data.

Example usage

"The discussion framed adaptive enrichment sequencing as a bandit-style reinforcement learning problem over candidate regions."

AI Agent Machine Learning Neural Network Supervised Learning Tool Calling
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Sutton RS, Barto AG. (2018). Reinforcement Learning: An Introduction. MIT Press.
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