Deep RL Agent for a Real-Time Action Strategy Game
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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 ± 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.
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