SOTAVerified

Starcraft II

Starcraft II is a RTS game; the task is to train an agent to play the game.

( Image credit: The StarCraft Multi-Agent Challenge )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–150 of 175 papers

TitleStatusHype
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
Off-Beat Multi-Agent Reinforcement Learning—0
Learning to Guide Multiple Heterogeneous Actors from a Single Human Demonstration via Automatic Curriculum Learning in StarCraft II—0
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning—0
Learning to Transfer Role Assignment Across Team Sizes—0
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed AgentsCode0
Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation—0
MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement LearningCode0
FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent SystemsCode0
Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy GradientsCode0
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning—0
CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning—0
Cooperative Multi-Agent Reinforcement Learning with Hypergraph ConvolutionCode0
RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents—0
On games and simulators as a platform for development of artificial intelligence for command and control—0
Containerized Distributed Value-Based Multi-Agent Reinforcement Learning—0
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism—0
Leveraging Transformers for StarCraft Macromanagement Prediction—0
Role Diversity Matters: A Study of Cooperative Training Strategies for Multi-Agent RL—0
MARNET: Backdoor Attacks against Value-Decomposition Multi-Agent Reinforcement Learning—0
An Approach to Partial Observability in Games: Learning to Both Act and Observe—0
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning—0
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment—0
Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural Architectures for Real-Time Strategy Games—0
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning—0
Deep Convolution for Irregularly Sampled Temporal Point Clouds—0
NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning—0
Regularized Softmax Deep Multi-Agent Q-Learning—0
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents—0
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning—0
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients—0
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning—0
Spatially Structured Recurrent Modules—0
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II—0
Reinforcement Learning for the Beginning of Starcraft II Game—0
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning—0
Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning—0
Spatially Structured Recurrent Modules—0
BGC: Multi-Agent Group Belief with Graph Clustering—0
Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection—0
S2RMs: Spatially Structured Recurrent Modules—0
StarCraft II Build Order Optimization using Deep Reinforcement Learning and Monte-Carlo Tree Search—0
Incorporating Pragmatic Reasoning Communication into Emergent Language—0
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization—0
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning—0
A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets—0
Show:102550
← PrevPage 3 of 4Next →

No leaderboard results yet.