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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
A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets—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
Robust Multi-Agent Reinforcement Learning by Mutual Information Regularization—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
Exact Reduction of Huge Action Spaces in General Reinforcement Learning—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
Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games—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
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
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play—0
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees—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
MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning—0
Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations—0
Maximum Correntropy Value Decomposition for Multi-agent Deep Reinforcemen Learning—0
Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning—0
Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning—0
MAIDCRL: Semi-centralized Multi-Agent Influence Dense-CNN Reinforcement 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
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning—0
MARL-LNS: Cooperative Multi-agent Reinforcement Learning via Large Neighborhoods Search—0
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