Abstract: A single policy can be trained to handle the user selection of parameters across a predetermined range for each component of an artificial intelligent agent within a domain. The agent can be trained across a number of weights within the desired range for each component. These weights determine how much of a reward portion for each component should be considered by the agent during training. Thus, an improved formulation can be realized for UVFA-like goals based on compositional reward functions parameterized by their components' weights. Additionally, a set of reward components has been determined for the domain of autonomous racing games that, when combined with the improved UVFA formulation, allows training a single racing agent that generalizes over continuous behaviors in multiple dimensions. This can be used by game designers to tune the skill and personality of a trained agent.

Abstract: A method for training an agent uses a mixture of scenarios designed to teach specific skills helpful in a larger domain, such as mixing general racing and very specific tactical racing scenarios. Aspects of the methods can include one or more of the following: (1) training the agent to be very good at time trials by having one or more cars spread out on the track; (2) running the agent in various racing scenarios with a variable number of opponents starting in different configurations around the track; (3) varying the opponents by using game-provided agents, agents trained according to aspects of the present invention, or agents controlled to follow specific driving lines; (4) setting up specific short scenarios with opponents in various racing situations with specific success criteria; and (5) having a dynamic curriculum based on how the agent performs on a variety of evaluation scenarios.