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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 201–225 of 311 papers

TitleStatusHype
Deep reinforcement learning with relational inductive biases—0
Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation—0
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning—0
Disturbing Reinforcement Learning Agents with Corrupted Rewards—0
Divergence-Regularized Multi-Agent Actor-Critic—0
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients—0
Dungeon Crawl Stone Soup as an Evaluation Domain for Artificial Intelligence—0
Dynamic Sight Range Selection in Multi-Agent Reinforcement Learning—0
Efficiently Scanning and Resampling Spatio-Temporal Tasks with Irregular Observations—0
Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation—0
Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks—0
Equivariant MuZero—0
Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi—0
Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games—0
Exact Reduction of Huge Action Spaces in General Reinforcement Learning—0
Exploiting Semantic Epsilon Greedy Exploration Strategy in Multi-Agent Reinforcement Learning—0
Exploring Adaptive MCTS with TD Learning in miniXCOM—0
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning—0
Factored Action Spaces in Deep Reinforcement Learning—0
Fidelity-Induced Interpretable Policy Extraction for Reinforcement Learning—0
Forecasting Evolution of Clusters in Game Agents with Hebbian Learning—0
From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI—0
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning—0
Grounded Answers for Multi-agent Decision-making Problem through Generative World Model—0
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism—0
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