MimicBot: Combining Imitation and Reinforcement Learning to win in Bot Bowl

Abstract

This paper describe an hybrid agent trained to play in Fantasy Football AI which participated in the Bot Bowl III competition. The agent, MimicBot, is implemented using a specifically designed deep policy network and trained using a combination of imitation and reinforcement learning. Previous attempts in using a reinforcement learning approach in such context failed for a number of reasons, e.g. due to the intrinsic randomness in the environment and the large and uneven number of actions available, with a curriculum learning approach failing to consistently beat a randomly paying agent. Currently no machine learning approach can beat a scripted bot which makes use of the domain knowledge on the game. Our solution, thanks to an imitation learning and a hybrid decision-making process, consistently beat such scripted agents. Moreover we shed lights on how to more efficiently train in a reinforcement learning setting while drastically increasing sample efficiency. MimicBot is the winner of the Bot Bowl III competition, and it is currently the state-of-the-art solution.

References in corpus (9)

• A Brief Survey of Deep Reinforcement Learning
• A Survey on Visual Transformer
• Trust Region Policy Optimization
• Dota 2 with Large Scale Deep Reinforcement Learning
• StarCraft II: A New Challenge for Reinforcement Learning
• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
• One-Shot Imitation Learning
• Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation
• What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study