An Efficient Agent Created In Starcraft 2 Using Pysc2
Main Article Content
Abstract
This paper introduces "Judy"(Bot/Agent that is developed using reinforcement learning algorithms using the deep mind toolset PySc2 within StarCraft II. The domain reinforcement learning is a unique uncharted field clustered with difficult problems than worked on in previously done research. By observing the actions and then associating them with reward specs within the SC2 domain we aim to supply an open Python-based GUI which consists of an In-house engine to interact with humans. Adding up to the aforementioned challenges the agent must tackle multiple game maps, using the set of mini-games that specialize in totally different parts of StarCraft II gameplay. The agent relies on game replays of humans who are skilled. By offering a starting or base result for a neural network trained by the acquired info we tend to predict the final outcomes and actions of the players. Thus, the agent experiences a brand new difficulty surrounding the environment exploring deep reinforcement learning algorithms.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.