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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 101–150 of 311 papers

TitleStatusHype
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment—0
AIIR-MIX: Multi-Agent Reinforcement Learning Meets Attention Individual Intrinsic Reward Mixing Network—0
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play—0
Fidelity-Induced Interpretable Policy Extraction for Reinforcement Learning—0
Cooperative Multi-Agent Planning with Adaptive Skill Synthesis—0
Cooperative Exploration for Multi-Agent Deep Reinforcement Learning—0
Factored Action Spaces in Deep Reinforcement Learning—0
Forecasting Evolution of Clusters in Game Agents with Hebbian Learning—0
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
Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning—0
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning—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
Exploring Adaptive MCTS with TD Learning in miniXCOM—0
Action Categorization for Computationally Improved Task Learning and Planning—0
Exploiting Semantic Epsilon Greedy Exploration Strategy in Multi-Agent Reinforcement Learning—0
Containerized Distributed Value-Based Multi-Agent Reinforcement Learning—0
Artificial Intelligence is stupid and causal reasoning won't fix it—0
Meta-Learning for Planning: Automatic Synthesis of Sample Based Planners—0
Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration—0
Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection—0
Exact Reduction of Huge Action Spaces in General Reinforcement Learning—0
Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning—0
High-Level Strategy Selection under Partial Observability in StarCraft: Brood War—0
Human-like Bots for Tactical Shooters Using Compute-Efficient Sensors—0
Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning—0
Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games—0
Composable Planning with Attributes—0
Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi—0
Equivariant MuZero—0
Combat Models for RTS Games—0
AI and Wargaming—0
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization—0
Modular Architecture for StarCraft II with Deep Reinforcement Learning—0
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks—0
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization—0
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation—0
Collaborative AI Teaming in Unknown Environments via Active Goal Deduction—0
Approximation Models of Combat in StarCraft 2—0
COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations—0
MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search—0
Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations—0
MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning—0
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning—0
Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning—0
Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning—0
Dungeon Crawl Stone Soup as an Evaluation Domain for Artificial Intelligence—0
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients—0
Anticipatory Thinking Challenges in Open Worlds: Risk Management—0
MAIDCRL: Semi-centralized Multi-Agent Influence Dense-CNN Reinforcement Learning—0
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