A method, referred to herein as upside down reinforcement learning (UDRL), includes: initializing a set of parameters for a computer-based learning model; providing a command input into the computer-based learning model as part of a trial, wherein the command input calls for producing a specified reward within a specified amount of time in an environment external to the computer-based learning model; producing an output with the computer-based learning model based on the command input; and utilizing the output to cause an action in the environment external to the computer-based learning model. Typically, during training, the command inputs (e.g., “get so much desired reward within so much time,” or more complex command inputs) are retrospectively adjusted to match what was really observed.
UPSIDE-DOWN REINFORCEMENT LEARNING