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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 151–175 of 311 papers

TitleStatusHype
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games—0
The Adversarial Resilience Learning Architecture for AI-based Modelling, Exploration, and Operation of Complex Cyber-Physical Systems—0
Towards a Deep Reinforcement Learning Approach for Tower Line Wars—0
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization—0
Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning—0
Transferable Curricula through Difficulty Conditioned Generators—0
Tree Search for Simultaneous Move Games via Equilibrium Approximation—0
Truthful Self-Play—0
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning—0
Unsupervised Hebbian Learning on Point Sets in StarCraft II—0
Value Propagation Networks—0
Variational Offline Multi-agent Skill Discovery—0
"Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us?—0
Neuroevolution for RTS Micro—0
Nucleolus Credit Assignment for Effective Coalitions in Multi-agent Reinforcement Learning—0
SrSv: Integrating Sequential Rollouts with Sequential Value Estimation for Multi-agent Reinforcement Learning—0
Action Categorization for Computationally Improved Task Learning and Planning—0
Adversary agent reinforcement learning for pursuit-evasion—0
AI and Wargaming—0
AIIR-MIX: Multi-Agent Reinforcement Learning Meets Attention Individual Intrinsic Reward Mixing Network—0
Aligning Individual and Collective Objectives in Multi-Agent Cooperation—0
An Approach to Partial Observability in Games: Learning to Both Act and Observe—0
A Narration-based Reward Shaping Approach using Grounded Natural Language Commands—0
Anticipatory Thinking Challenges in Open Worlds: Risk Management—0
Approximation Models of Combat in StarCraft 2—0
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