Incorporating Rivalry in Reinforcement Learning for a Competitive Game

11/02/2020
by   Pablo Barros, et al.
25

Recent advances in reinforcement learning with social agents have allowed us to achieve human-level performance on some interaction tasks. However, most interactive scenarios do not have as end-goal performance alone; instead, the social impact of these agents when interacting with humans is as important and, in most cases, never explored properly. This preregistration study focuses on providing a novel learning mechanism based on a rivalry social impact. Our scenario explored different reinforcement learning-based agents playing a competitive card game against human players. Based on the concept of competitive rivalry, our analysis aims to investigate if we can change the assessment of these agents from a human perspective.

READ FULL TEXT

page 1

page 2

page 3

page 4

04/08/2020

Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card Game

Learning how to adapt to complex and dynamic environments is one of the ...
10/22/2016

Reinforcement Learning in Conflicting Environments for Autonomous Vehicles

In this work, we investigate the application of Reinforcement Learning t...
11/10/2018

Learning Shaping Strategies in Human-in-the-loop Interactive Reinforcement Learning

Providing reinforcement learning agents with informationally rich human ...
04/21/2021

Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents

In this article we study the problem of training intelligent agents usin...
09/24/2021

Go-Blend behavior and affect

This paper proposes a paradigm shift for affective computing by viewing ...
06/28/2019

No-boarding buses: Agents allowed to cooperate or defect

We study a bus system with a no-boarding policy, where a "slow" bus may ...
03/25/2021

Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents

As modern games continue growing both in size and complexity, it has bec...