Triple

Target class: learning environment model
Context triple: [SOLE, instanceOf, learning environment model]

• A. model-based reinforcement learning algorithm
  A model-based reinforcement learning algorithm is a decision-making method that learns or uses an explicit model of the environment’s dynamics to plan and select actions that maximize long-term rewards.
• B. Monte Carlo reinforcement learning algorithm
  A Monte Carlo reinforcement learning algorithm is a method that learns optimal policies by estimating value functions from complete, sampled episodes of experience without requiring a model of the environment’s dynamics.
• C. agent-environment cycle API
  An agent-environment cycle API defines the interfaces and protocols that let an autonomous agent repeatedly perceive state from an environment, decide on actions, and apply those actions to produce the next state in a closed feedback loop.
• D. learning rule
  A learning rule is a formal method or algorithm that specifies how a system updates its internal parameters or representations based on experience or data to improve performance over time.
• E. reinforcement learning library
  A reinforcement learning library is a software toolkit that provides algorithms, environments, and utilities to design, train, evaluate, and deploy agents that learn optimal behaviors through trial-and-error interactions with their environment.
• F. None of above. chosen