DeepAI AI Chat
Log In Sign Up

MarioMix: Creating Aligned Playstyles for Bots with Interactive Reinforcement Learning

by   Christian Arzate Cruz, et al.

In this paper, we propose a generic framework that enables game developers without knowledge of machine learning to create bot behaviors with playstyles that align with their preferences. Our framework is based on interactive reinforcement learning (RL), and we used it to create a behavior authoring tool called MarioMix. This tool enables non-experts to create bots with varied playstyles for the game titled Super Mario Bros. The main interaction procedure of MarioMix consists of presenting short clips of gameplay displaying precomputed bots with different playstyles to end-users. Then, end-users can select the bot with the playstyle that behaves as intended. We evaluated MarioMix by incorporating input from game designers working in the industry.


Interactive Explanations: Diagnosis and Repair of Reinforcement Learning Based Agent Behaviors

Reinforcement learning techniques successfully generate convincing agent...

A Survey on Interactive Reinforcement Learning: Design Principles and Open Challenges

Interactive reinforcement learning (RL) has been successfully used in va...

The PlayStation Reinforcement Learning Environment (PSXLE)

We propose a new benchmark environment for evaluating Reinforcement Lear...

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

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

ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents

As reinforcement learning methods increasingly amass accomplishments, th...

FireCommander: An Interactive, Probabilistic Multi-agent Environment for Joint Perception-Action Tasks

The purpose of this tutorial is to help individuals use the FireCommande...