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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
Hierarchical Strategies for Cooperative Multi-Agent Reinforcement Learning—0
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games—0
CURO: Curriculum Learning for Relative Overgeneralization—0
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning—0
Decision-making with Speculative Opponent ModelsCode0
Value-based CTDE Methods in Symmetric Two-team Markov Game: from Cooperation to Team CompetitionCode0
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy GradientCode0
Exploring Adaptive MCTS with TD Learning in miniXCOM—0
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees—0
Taming Multi-Agent Reinforcement Learning with Estimator Variance Reduction—0
Forecasting Evolution of Clusters in Game Agents with Hebbian Learning—0
A Framework for Understanding and Visualizing Strategies of RL AgentsCode0
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2—0
Maximum Correntropy Value Decomposition for Multi-agent Deep Reinforcemen Learning—0
Unsupervised Hebbian Learning on Point Sets in StarCraft II—0
Evolutionary Game-Theoretical Analysis for General Multiplayer Asymmetric Games—0
PAC: Assisted Value Factorisation with Counterfactual Predictions in Multi-Agent Reinforcement LearningCode0
On the Limitations of Elo: Real-World Games, are Transitive, not AdditiveCode0
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?—0
Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis—0
Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning—0
Policy Diagnosis via Measuring Role Diversity in Cooperative Multi-agent RL—0
DM^2: Decentralized Multi-Agent Reinforcement Learning for Distribution MatchingCode0
Off-Beat Multi-Agent Reinforcement Learning—0
Terrain Analysis in StarCraft 1 and 2 as Combinatorial OptimizationCode0
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