Deep RL Agent for a Real-Time Action Strategy Game

Abstract

We introduce a reinforcement learning environment based on Heroic - Magic Duel, a 1 v 1 action strategy game. This domain is non-trivial for several reasons: it is a real-time game, the state space is large, the information given to the player before and at each step of a match is imperfect, and distribution of actions is dynamic. Our main contribution is a deep reinforcement learning agent playing the game at a competitive level that we trained using PPO and self-play with multiple competing agents, employing only a simple reward of $\pm 1$ depending on the outcome of a single match. Our best self-play agent, obtains around $65\%$ win rate against the existing AI and over $50\%$ win rate against a top human player.

References in corpus (7)

• Playing Atari with Deep Reinforcement Learning
• High-Dimensional Continuous Control Using Generalized Advantage Estimation
• Asynchronous Methods for Deep Reinforcement Learning
• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
• Dota 2 with Large Scale Deep Reinforcement Learning
• StarCraft II: A New Challenge for Reinforcement Learning
• Emergent Complexity via Multi-Agent Competition