Meta Arcade: A Configurable Environment Suite for Meta-Learning

12/01/2021
by   Edward W. Staley, et al.
0

Most approaches to deep reinforcement learning (DRL) attempt to solve a single task at a time. As a result, most existing research benchmarks consist of individual games or suites of games that have common interfaces but little overlap in their perceptual features, objectives, or reward structures. To facilitate research into knowledge transfer among trained agents (e.g. via multi-task and meta-learning), more environment suites that provide configurable tasks with enough commonality to be studied collectively are needed. In this paper we present Meta Arcade, a tool to easily define and configure custom 2D arcade games that share common visuals, state spaces, action spaces, game components, and scoring mechanisms. Meta Arcade differs from prior environments in that both task commonality and configurability are prioritized: entire sets of games can be constructed from common elements, and these elements are adjustable through exposed parameters. We include a suite of 24 predefined games that collectively illustrate the possibilities of this framework and discuss how these games can be configured for research applications. We provide several experiments that illustrate how Meta Arcade could be used, including single-task benchmarks of predefined games, sample curriculum-based approaches that change game parameters over a set schedule, and an exploration of transfer learning between games.

READ FULL TEXT

page 4

page 5

page 6

page 10

page 11

page 12

page 16

research
05/12/2022

Multi-Environment Meta-Learning in Stochastic Linear Bandits

In this work we investigate meta-learning (or learning-to-learn) approac...
research
04/08/2019

Creating Pro-Level AI for Real-Time Fighting Game with Deep Reinforcement Learning

Reinforcement learning combined with deep neural networks has performed ...
research
05/06/2021

Meta-Learning-based Deep Reinforcement Learning for Multiobjective Optimization Problems

Deep reinforcement learning (DRL) has recently shown its success in tack...
research
09/28/2022

Meta-Learning in Games

In the literature on game-theoretic equilibrium finding, focus has mainl...
research
10/03/2019

Generalized Inner Loop Meta-Learning

Many (but not all) approaches self-qualifying as "meta-learning" in deep...
research
02/04/2021

Alchemy: A structured task distribution for meta-reinforcement learning

There has been rapidly growing interest in meta-learning as a method for...
research
08/12/2020

Learning to Reason in Round-based Games: Multi-task Sequence Generation for Purchasing Decision Making in First-person Shooters

Sequential reasoning is a complex human ability, with extensive previous...

Please sign up or login with your details

Forgot password? Click here to reset