8 research outputs found
Monte-Carlo tree search for persona based player modeling
Is it possible to conduct player modeling without any players?
In this paper we use Monte-Carlo Tree Search-controlled
procedural personas to simulate a range of decision making
styles in the puzzle game MiniDungeons 2. The purpose is
to provide a method for synthetic play testing of game levels
with synthetic players based on designer intuition and experience.
Five personas are constructed, representing five different
decision making styles archetypal for the game. The personas
vary solely in the weights of decision-making utilities
that describe their valuation of a set affordances in MiniDungeons
2. By configuring these weights using designer expert
knowledge, and passing the configurations directly to the
MCTS algorithm, we make the personas exhibit a number of
distinct decision making and play styles.The research was supported, in part, by the FP7 ICT project
C2Learn (project no: 318480), the FP7 Marie Curie CIG
project AutoGameDesign (project no: 630665), and by the
Stibo Foundation Travel Bursary Grant for Global IT Talents.peer-reviewe
Action Guidance with MCTS for Deep Reinforcement Learning
Deep reinforcement learning has achieved great successes in recent years,
however, one main challenge is the sample inefficiency. In this paper, we focus
on how to use action guidance by means of a non-expert demonstrator to improve
sample efficiency in a domain with sparse, delayed, and possibly deceptive
rewards: the recently-proposed multi-agent benchmark of Pommerman. We propose a
new framework where even a non-expert simulated demonstrator, e.g., planning
algorithms such as Monte Carlo tree search with a small number rollouts, can be
integrated within asynchronous distributed deep reinforcement learning methods.
Compared to a vanilla deep RL algorithm, our proposed methods both learn faster
and converge to better policies on a two-player mini version of the Pommerman
game.Comment: AAAI Conference on Artificial Intelligence and Interactive Digital
Entertainment (AIIDE'19). arXiv admin note: substantial text overlap with
arXiv:1904.05759, arXiv:1812.0004