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| StarCraft II Learning Environment | |
|---|---|
| Name | StarCraft II Learning Environment |
| Developer | DeepMind |
| Released | 2017 |
| Programming language | Python, C++ |
| Platform | Linux, macOS, Windows |
| Genre | Reinforcement learning environment |
StarCraft II Learning Environment The StarCraft II Learning Environment is a research platform created to enable artificial intelligence experiments on the real-time strategy game StarCraft II. It was developed for use by academic institutions, industry labs, and independent researchers to study reinforcement learning, multi-agent coordination, and hierarchical planning within a complex simulated environment.
The platform was announced by DeepMind in collaboration with Blizzard Entertainment and built to support work from organizations like Google DeepMind, University of Oxford, Massachusetts Institute of Technology, Stanford University, and University of California, Berkeley. Its release followed precedents set by projects such as Atari 2600 environments used by researchers at University College London, the ImageNet efforts from Stanford University and Princeton University, and reinforcement learning benchmarks from OpenAI. It gained attention alongside research from institutions including Carnegie Mellon University, University of Toronto, University of Montreal, ETH Zurich, University of Cambridge, Tsinghua University, Peking University, National University of Singapore, University of Washington, Columbia University, and California Institute of Technology. Early adopters included teams at Facebook AI Research and Microsoft Research.
The environment exposes a programmable backend that interfaces with the StarCraft II engine developed by Blizzard Entertainment and integrates with research stacks used by DeepMind and academic labs. The architecture supports distributed training paradigms used in projects at Google Research and parallels systems from OpenAI and DeepMind such as infrastructure for AlphaGo and AlphaZero. It supports modular components comparable to middleware used by ROS in robotics research at Massachusetts Institute of Technology and orchestration patterns similar to Kubernetes employed by teams at Google. The system design reflects software engineering practices from companies like NVIDIA and Intel and academic contributions from Princeton University and UC Berkeley.
The API provides access compatible with Python toolchains widely adopted at Stanford University, Carnegie Mellon University, and ETH Zurich. It uses Protobuf-style serialization patterns informed by engineers at Google and supports extensions used by groups at University of Toronto and University of Montreal. Interfaces accommodate integrations with frameworks developed at Facebook AI Research, OpenAI, DeepMind, and research codebases from Microsoft Research and IBM Research. The environment exposes observation and action spaces that echo conventions implemented in environments like those from OpenAI Gym, utilized by researchers at Berkeley AI Research and NYU.
The platform includes scenarios used to test algorithms developed at DeepMind, OpenAI, Google Brain, Facebook AI Research, and universities including MIT, Stanford, Carnegie Mellon University, and University of Oxford. Benchmarks have been proposed in papers from Nature-affiliated groups, conferences such as NeurIPS, ICML, ICLR, and venues like AAAI and IJCAI. Tasks include micromanagement and macromanagement challenges similar to benchmarks used at DeepMind for AlphaStar research and by academic groups at University of Toronto and Tsinghua University. Competitions and shared tasks have been organized at workshops associated with NeurIPS and ICML where teams from ETH Zurich, University of Cambridge, Peking University, National University of Singapore, and University of Washington have submitted results.
Research leveraging the environment spans work by labs such as DeepMind, OpenAI, Facebook AI Research, Microsoft Research, IBM Research, and academic groups at MIT, Stanford, UC Berkeley, Carnegie Mellon University, University of Oxford, University of Toronto, and ETH Zurich. Applications include multi-agent coordination papers in conference proceedings at NeurIPS and ICML, imitation learning studies linked to work at CMU and Stanford, and hierarchical reinforcement learning inspired by earlier research from Berkeley AI Research and Google DeepMind. Cross-disciplinary collaborations involved researchers from institutions like Princeton University, Columbia University, Peking University, Tsinghua University, National University of Singapore, and industrial partners such as NVIDIA and Intel.
Evaluation protocols draw on metrics used in benchmark suites at NeurIPS and ICML and scoring methodologies found in competitions organized by Blizzard Entertainment and research festivals at ACM conferences. Common metrics include win rate comparisons used in research by DeepMind and OpenAI, episode reward aggregates familiar to groups at Berkeley AI Research and Stanford University, and sample efficiency measures employed by teams at Google Research and Microsoft Research. Leaderboards and reproducibility challenges have paralleled community efforts from OpenAI, DeepMind, Facebook AI Research, and academic consortia at University of Oxford and MIT.
Critiques of the platform have been raised by researchers at institutions like Harvard University, Princeton University, University of Cambridge, ETH Zurich, and Carnegie Mellon University concerning simulation fidelity, generalization, and compute resource demands. Concerns mirror discussions in policy and ethics forums at AAAI and NeurIPS workshops and debates involving stakeholders such as Google DeepMind, OpenAI, and Microsoft Research. Other limitations noted by teams at University of Toronto, Tsinghua University, Peking University, and National University of Singapore include reproducibility challenges and constraints when translating learned policies to other domains studied at MIT and Stanford University.
Category:Reinforcement learning environments