Abstract: Methods and apparatus for the application of reinforcement learning to animal medical diagnostics. In one embodiment, a system is disclosed that utilizes two (2) RL agents that are arranged sequentially and that are optimized independently from one another. The first RL agent is an assessment RL agent which takes as input from one or more of: outputs from classification artificial intelligence (AI) engines; outputs from subjective biological data storage devices, and outputs from objective biological data storage devices. Using these input(s), the assessment RL agent outputs a set of assessments which evaluate a set of conditions associated with an animal. This set of assessments is then provided as input to a second RL agent known-as a plan RL agent which determines a set of treatment recommendations and diagnostics based off this determined set of assessments. Methods and computer-readable media are also disclosed.

Abstract: Methods and apparatus for the application of reinforcement learning to animal medical diagnostics. In one embodiment, a system is disclosed that utilizes two (2) RL agents that are arranged sequentially and that are optimized independently from one another. The first RL agent is an assessment RL agent which takes as input from one or more of: outputs from classification artificial intelligence (AI) engines; outputs from subjective biological data storage devices, and outputs from objective biological data storage devices. Using these input(s), the assessment RL agent outputs a set of assessments which evaluate a set of conditions associated with an animal. This set of assessments is then provided as input to a second RL agent known-as a plan RL agent which determines a set of treatment recommendations and diagnostics based off this determined set of assessments. Methods and computer-readable media are also disclosed.

Abstract: Methods and apparatus for the application of reinforcement learning to animal medical diagnostics. In one embodiment, a system is disclosed that utilizes two (2) RL agents that are arranged sequentially and that are optimized independently from one another. The first RL agent is an assessment RL agent which takes as input from one or more of: outputs from classification artificial intelligence (AI) engines; outputs from subjective biological data storage devices, and outputs from objective biological data storage devices. Using these input(s), the assessment RL agent outputs a set of assessments which evaluate a set of conditions associated with an animal. This set of assessments is then provided as input to a second RL agent known-as a plan RL agent which determines a set of treatment recommendations and diagnostics based off this determined set of assessments. Methods and computer-readable media are also disclosed.