Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep RL Agent for a Real-Time Action Strategy Game   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.

35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci