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Starcraft

Starcraft I is a RTS game; the task is to train an agent to play the game.

( Image credit: Macro Action Selection with Deep Reinforcement Learning in StarCraft )

Papers

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Showing 251–300 of 311 papers

TitleStatusHype
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning—0
From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI—0
A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets—0
Reinforcement Learning-based Application Autoscaling in the Cloud: A Survey—0
Heterogeneous Learning from Demonstration—0
SMIX(λ): Enhancing Centralized Value Functions for Cooperative Multi-Agent Reinforcement LearningCode0
A Narration-based Reward Shaping Approach using Grounded Natural Language Commands—0
A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement LearningCode0
Learning Nearly Decomposable Value Functions Via Communication MinimizationCode0
Deep Coordination GraphsCode0
Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based ControlCode0
Action Semantics Network: Considering the Effects of Actions in Multiagent SystemsCode0
Arena: a toolkit for Multi-Agent Reinforcement LearningCode0
Learning a Behavioral Repertoire from Demonstrations—0
Growing Action SpacesCode0
Explainable Reinforcement Learning Through a Causal LensCode0
Deep reinforcement learning with relational inductive biases—0
Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning—0
Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer LearningCode0
Neural-encoding Human Experts' Domain Knowledge to Warm Start Reinforcement LearningCode0
Dungeon Crawl Stone Soup as an Evaluation Domain for Artificial Intelligence—0
The Natural Language of ActionsCode0
StarAlgo: A Squad Movement Planning Library for StarCraft using Monte Carlo Tree Search and NegamaxCode0
Learning when to Communicate at Scale in Multiagent Cooperative and Competitive TasksCode0
Variational Saccading: Efficient Inference for Large Resolution ImagesCode0
Macro action selection with deep reinforcement learning in StarCraftCode0
Forward Modeling for Partial Observation Strategy Games - A StarCraft DefoggerCode0
Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-DecodersCode0
High-Level Strategy Selection under Partial Observability in StarCraft: Brood War—0
Modular Architecture for StarCraft II with Deep Reinforcement Learning—0
Multi-Agent Common Knowledge Reinforcement LearningCode0
On Reinforcement Learning for Full-length Game of StarCraft—0
TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full GameCode0
Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy GamesCode0
Asynchronous Advantage Actor-Critic Agent for Starcraft II—0
Relational Deep Reinforcement LearningCode0
Value Propagation Networks—0
Action Categorization for Computationally Improved Task Learning and Planning—0
StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer LearningCode0
Neuroevolution for RTS Micro—0
Multi-objective evolution for 3D RTS Micro—0
Composable Planning with Attributes—0
Multi-platform Version of StarCraft: Brood War in a Docker Container: Technical ReportCode0
RedDwarfData: a simplified dataset of StarCraft matchesCode0
Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning—0
Towards a Deep Reinforcement Learning Approach for Tower Line Wars—0
MSC: A Dataset for Macro-Management in StarCraft IICode0
StarCraft II: A New Challenge for Reinforcement LearningCode0
STARDATA: A StarCraft AI Research DatasetCode0
Learning Macromanagement in StarCraft from Replays using Deep Learning—0
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