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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 101–125 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
Language-Guided Multi-Agent Learning in Simulations: A Unified Framework and Evaluation—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
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
Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games—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
Composable Planning with Attributes—0
Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning—0
High-Level Strategy Selection under Partial Observability in StarCraft: Brood War—0
Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi—0
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