Lucy-SKG: Learning to Play Rocket League Efficiently Using Deep Reinforcement Learning

TL;DR

Lucy-SKG is a reinforcement learning agent that efficiently learned to play Rocket League, outperforming top existing bots by leveraging novel reward shaping, neural architectures, and thorough ablation studies.

Contribution

The paper introduces Lucy-SKG, a state-of-the-art RL agent for Rocket League, with novel reward functions, neural architectures, and comprehensive ablation analysis for sample-efficient learning.

Findings

Outperforms top existing Rocket League bots

Demonstrates effectiveness of reward shaping and auxiliary architectures

Shows potential of RL in complex multiplayer game control

Abstract

A successful tactic that is followed by the scientific community for advancing AI is to treat games as problems, which has been proven to lead to various breakthroughs. We adapt this strategy in order to study Rocket League, a widely popular but rather under-explored 3D multiplayer video game with a distinct physics engine and complex dynamics that pose a significant challenge in developing efficient and high-performance game-playing agents. In this paper, we present Lucy-SKG, a Reinforcement Learning-based model that learned how to play Rocket League in a sample-efficient manner, outperforming by a notable margin the two highest-ranking bots in this game, namely Necto (2022 bot champion) and its successor Nexto, thus becoming a state-of-the-art agent. Our contributions include: a) the development of a reward analysis and visualization library, b) novel parameterizable reward shape functions that capture the utility of complex reward types via our proposed Kinesthetic Reward Combination (KRC) technique, and c) design of auxiliary neural architectures for training on reward prediction and state representation tasks in an on-policy fashion for enhanced efficiency in learning speed and performance. By performing thorough ablation studies for each component of Lucy-SKG, we showed their independent effectiveness in overall performance. In doing so, we demonstrate the prospects and challenges of using sample-efficient Reinforcement Learning techniques for controlling complex dynamical systems under competitive team-based multiplayer conditions.