DeepAI AI Chat
Log In Sign Up

Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents

04/21/2021
by   Alessandro Sestini, et al.
0

In this article we study the problem of training intelligent agents using Reinforcement Learning for the purpose of game development. Unlike systems built to replace human players and to achieve super-human performance, our agents aim to produce meaningful interactions with the player, and at the same time demonstrate behavioral traits as desired by game designers. We show how to combine distinct behavioral policies to obtain a meaningful "fusion" policy which comprises all these behaviors. To this end, we propose four different policy fusion methods for combining pre-trained policies. We further demonstrate how these methods can be used in combination with Inverse Reinforcement Learning in order to create intelligent agents with specific behavioral styles as chosen by game designers, without having to define many and possibly poorly-designed reward functions. Experiments on two different environments indicate that entropy-weighted policy fusion significantly outperforms all others. We provide several practical examples and use-cases for how these methods are indeed useful for video game production and designers.

READ FULL TEXT

page 1

page 2

page 3

page 4

page 5

page 6

page 7

page 8

11/21/2022

Examining Policy Entropy of Reinforcement Learning Agents for Personalization Tasks

This effort is focused on examining the behavior of reinforcement learni...
03/25/2019

Winning Isn't Everything: Enhancing Game Development with Intelligent Agents

Recently, there have been several high-profile achievements of agents le...
08/26/2022

Generative Personas That Behave and Experience Like Humans

Using artificial intelligence (AI) to automatically test a game remains ...
11/02/2020

Incorporating Rivalry in Reinforcement Learning for a Competitive Game

Recent advances in reinforcement learning with social agents have allowe...
02/01/2023

Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning

Multiagent reinforcement learning (MARL) has benefited significantly fro...
06/02/2019

Automated Video Game Testing Using Synthetic and Human-Like Agents

In this paper, we present a new methodology that employs tester agents t...
07/01/2018

Beyond Winning and Losing: Modeling Human Motivations and Behaviors Using Inverse Reinforcement Learning

In recent years, reinforcement learning (RL) methods have been applied t...