Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loop

• URL: http://arxiv.org/abs/2007.10504v1
• Date: Mon, 20 Jul 2020 21:59:53 GMT
• Title: Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loop
• Authors: Jonathan Chung, Anna Luo, Xavier Raffin, Scott Perry
• Abstract summary: The Battlesnake Challenge is a framework for multi-agent reinforcement learning with Human-In-the-Loop (HILL) We develop a simulated game environment for the offline multi-agent model training and identify a set of baselines that can be instilled to improve learning. Our results show that agents with the proposed HILL consistently outperform agents without HILL.
• Score: 2.9691097886836944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present the Battlesnake Challenge, a framework for multi-agent reinforcement learning with Human-In-the-Loop Learning (HILL). It is developed upon Battlesnake, a multiplayer extension of the traditional Snake game in which 2 or more snakes compete for the final survival. The Battlesnake Challenge consists of an offline module for model training and an online module for live competitions. We develop a simulated game environment for the offline multi-agent model training and identify a set of baseline heuristics that can be instilled to improve learning. Our framework is agent-agnostic and heuristics-agnostic such that researchers can design their own algorithms, train their models, and demonstrate in the online Battlesnake competition. We validate the framework and baseline heuristics with our preliminary experiments. Our results show that agents with the proposed HILL methods consistently outperform agents without HILL. Besides, heuristics of reward manipulation had the best performance in the online competition. We open source our framework at https://github.com/awslabs/sagemaker-battlesnake-ai.

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