The StarCraft II Learning Environment (S2LE) is a reinforcement learning environment based on the game StarCraft II. The environment consists of three sub-components: a Linux StarCraft II binary, the StarCraft II API and PySC2. The StarCraft II API allows programmatic control of StarCraft II. It can be used to start a game, get observations, take actions, and review replays. PyC2 is a Python environment that wraps the StarCraft II API to ease the interaction between Python reinforcement learning agents and StarCraft II. It defines an action and observation specification, and includes a random agent and a handful of rule-based agents as examples. It also includes some mini-games as challenges and visualization tools to understand what the agent can see and do. Source: https://github.com/deepmind/pysc2 Image Source: https://github.com/deepmind/pysc2
LANI is a 3D navigation environment and corpus, where an agent navigates between landmarks. Lani contains 27,965 crowd-sourced instructions for navigation in an open environment. Each datapoint includes an instruction, a human-annotated ground-truth demonstration trajectory, and an environment with various landmarks and lakes. The dataset train/dev/test split is 19,758/4,135/4,072. Each environment specification defines placement of 6–13 landmarks within a square grass field of size 50m×50m. Source: Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction Image Source: https://arxiv.org/pdf/1809.00786.pdf
The StarCraft Multi-Agent Challenges+ requires agents to learn completion of multi-stage tasks and usage of environmental factors without precise reward functions. The previous challenges (SMAC) recognized as a standard benchmark of Multi-Agent Reinforcement Learning are mainly concerned with ensuring that all agents cooperatively eliminate approaching adversaries only through fine manipulation with obvious reward functions. This challenge, on the other hand, is interested in the exploration capability of MARL algorithms to efficiently learn implicit multi-stage tasks and environmental factors as well as micro-control. This study covers both offensive and defensive scenarios. In the offensive scenarios, agents must learn to first find opponents and then eliminate them. The defensive scenarios require agents to use topographic features. For example, agents need to position themselves behind protective structures to make it harder for enemies to attack.
MSC is a dataset for Macro-Management in StarCraft 2 based on the platfrom SC2LE. It consists of well-designed feature vectors, pre-defined high-level actions and final result of each match. It contains 36,619 high quality replays, which are unbroken and played by relatively professional players. Source: https://github.com/wuhuikai/MSC
SC2EGSet: StarCraft II Esport Game State Dataset Pre-processed data that was generated from the SC2ReSet: StarCraft II Esports Replaypack Set Data Modeling Our aplication programing interface (API) implementation supports downloading, unpacking, loading and data access features. Please refer to: https://github.com/Kaszanas/SC2EGSet_Dataset License Information This dataset is licensed under the following license: Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)