Authorial Idioms for Target Distributions in TTD-MDPs

In designing Markov Decision Processes (MDP), one must define the world, its dynamics, a set of actions, and a reward function. MDPs are often applied in situations where there is a clear choice of reward functions and in these cases significant care must be taken to construct a reward function that induces the desired behavior. In this paper, we consider an analogous design problem: crafting a target distribution in Targeted Trajectory Distribution MDPs (TTD-MDPs). TTD-MDPs produce probabilistic policies that minimize divergence from a target distribution of trajectories from an underlying MDP. They are an extension of MDPs that provide variety of experience during repeated execution. Here, we present a brief overview of TTD-MDPs with approaches for constructing target distributions. Then we present a novel authorial idiom for creating target distributions using prototype trajectories. We evaluate these approaches on a drama manager for an interactive game.

• Type: Conference
• Year: 2007
• Where: AAAI
• Authors: David L. Roberts, Sooraj Bhat, Kenneth St. Clair, Charles Lee Isbell Jr.
• Keywords: AAAI 2007, Cases Significant Care, Intelligent Agents, Reward Function, Target Distributions