2 research outputs found
Macro action selection with deep reinforcement learning in StarCraft
StarCraft (SC) is one of the most popular and successful Real Time Strategy
(RTS) games. In recent years, SC is also widely accepted as a challenging
testbed for AI research because of its enormous state space, partially observed
information, multi-agent collaboration, and so on. With the help of annual
AIIDE and CIG competitions, a growing number of SC bots are proposed and
continuously improved. However, a large gap remains between the top-level bot
and the professional human player. One vital reason is that current SC bots
mainly rely on predefined rules to select macro actions during their games.
These rules are not scalable and efficient enough to cope with the enormous yet
partially observed state space in the game. In this paper, we propose a deep
reinforcement learning (DRL) framework to improve the selection of macro
actions. Our framework is based on the combination of the Ape-X DQN and the
Long-Short-Term-Memory (LSTM). We use this framework to build our bot, named as
LastOrder. Our evaluation, based on training against all bots from the AIIDE
2017 StarCraft AI competition set, shows that LastOrder achieves an 83% winning
rate, outperforming 26 bots in total 28 entrants
Macro action selection with deep reinforcement learning in StarCraft
StarCraft (SC) is one of the most popular and successful Real Time Strategy
(RTS) games. In recent years, SC is also considered as a testbed for AI
research, due to its enormous state space, hidden information, multi-agent
collaboration and so on. Thanks to the annual AIIDE and CIG competitions, a
growing number of bots are proposed and being continuously improved. However, a
big gap still remains between the top bot and the professional human players.
One vital reason is that current bots mainly rely on predefined rules to
perform macro actions. These rules are not scalable and efficient enough to
cope with the large but partially observed macro state space in SC. In this
paper, we propose a DRL based framework to do macro action selection. Our
framework combines the reinforcement learning approach Ape-X DQN with
Long-Short-Term-Memory (LSTM) to improve the macro action selection in bot. We
evaluate our bot, named as LastOrder, on the AIIDE 2017 StarCraft AI
competition bots set. Our bot achieves overall 83% win-rate, outperforming 26
bots in total 28 entrants