  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.
