Abstract: Embodiments disclosed include systems, apparatus, and/or methods to receive a target health status of and/or quality of bioproduct produced by a managed livestock and indications of health status and quality of bioproduct. The systems, apparatus, and/or methods generate a set of input vectors based on the target health status or bioproduct quality, and the indications of bioproduct quality, and health status, and provide the set of input vectors to a machine learning model trained to generate an output indicating a feed selection. The feed selection can be included in a feed blend and administered to the managed livestock, such that, upon consumption, it increases a likelihood of collectively improving the health status of the managed livestock and the bioproduct quality of the managed livestock.

Abstract: Reinforcement learning can be applied to generate hierarchical states. Inputs associated with interactions of an agent with an environment are received, where interactions include states and actions that cause state changes. An indication of a target state to achieve is received. A sequence including a first, second, and third state is identified, where the agent can perform a first action to transition from the first to second state, and a second action to transition from the second state to the third state. A hierarchical state can be generated, where a third action transitions from the first state to the hierarchical state, and a fourth action transactions from the hierarchical state to the third state.

Abstract: In some embodiments, a method includes receiving a target reproductive property associated with a managed livestock, and receiving an indication of health status of the managed livestock. The method further includes generating a set of input vectors based on the target reproductive property and the indication of health status. The method further includes providing the set of input vectors to a machine learning model trained to generate an output indicating a feed selection to be used to feed the managed livestock. The feed selection can be configured to, upon consumption, increase a likelihood of achieving the target reproductive property associated with the managed livestock. The method further includes administering a feed blend including the feed selection to the managed livestock.

Abstract: Embodiments disclosed include systems, apparatus, and/or methods to receive a target health status of and/or quality of bioproduct produced by a managed livestock and indications of health status and quality of bioproduct. The systems, apparatus, and/or methods generate a set of input vectors based on the target health status or bioproduct quality, and the indications of bioproduct quality, and health status, and provide the set of input vectors to a machine learning model trained to generate an output indicating a feed selection. The feed selection can be included in a feed blend and administered to the managed livestock, such that, upon consumption, it increases a likelihood of collectively improving the health status of the managed livestock and the bioproduct quality of the managed livestock.

Abstract: Embodiments disclosed include systems, apparatus, and/or methods to receive an indication of a target quality of a property associated with a bioproduct obtained from a managed livestock, the target quality being associated with an identified end-use, and generate a set of input vectors based on the target quality of the property. The systems, apparatus, and/or methods can be further configured to provide the set of input vectors to a machine learning model to generate an output indicating a feed selection and/or feed schedule selection to be used to feed the managed livestock. The feed selection can be such that, upon consumption, increase a likelihood of meeting the target quality of the property. In some embodiments, the systems, apparatus, and/or methods include implementation of features like temporal abstraction, auto-tuning of hyperparameters of model/agent, hierarchical/cognitive learning, synthetic state/trajectory generation and/or adaptive lookahead to achieve the desired output.