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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 151–200 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
Artificial Intelligence is stupid and causal reasoning won't fix it—0
Asynchronous Advantage Actor-Critic Agent for Starcraft II—0
Automatic Observer Script for StarCraft: Brood War Bot Games (technical report)—0
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms—0
BET: Explaining Deep Reinforcement Learning through The Error-Prone Decisions—0
Beyond Joint Demonstrations: Personalized Expert Guidance for Efficient Multi-Agent Reinforcement Learning—0
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis—0
Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning—0
COA-GPT: Generative Pre-trained Transformers for Accelerated Course of Action Development in Military Operations—0
Collaborative AI Teaming in Unknown Environments via Active Goal Deduction—0
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization—0
Combat Models for RTS Games—0
Composable Planning with Attributes—0
Containerized Distributed Value-Based Multi-Agent Reinforcement Learning—0
Cooperative Exploration for Multi-Agent Deep Reinforcement Learning—0
Cooperative Multi-Agent Planning with Adaptive Skill Synthesis—0
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment—0
Coordinated Multi-Agent Exploration Using Shared Goals—0
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
CURO: Curriculum Learning for Relative Overgeneralization—0
Data-Driven Distributed Common Operational Picture from Heterogeneous Platforms using Multi-Agent Reinforcement Learning—0
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning—0
Deep Convolution for Irregularly Sampled Temporal Point Clouds—0
Deep Reinforcement Learning, a textbook—0
Deep reinforcement learning with relational inductive biases—0
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