Papers with Code 아카이브
Starcraft II
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 3
- 보존된 평가 기록
- 3
Starcraft II is a RTS game; the task is to train an agent to play the game.
<span style="color:grey; opacity: 0.6">( Image credit: The StarCraft Multi-Agent Challenge )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents
Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation
MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning
FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent Systems
Value Functions Factorization with Latent State Information Sharing in Decentralized Multi-Agent Policy Gradients
Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning
CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning
Cooperative Multi-Agent Reinforcement Learning with Hypergraph Convolution
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration
Coordinated Proximal Policy Optimization
On games and simulators as a platform for development of artificial intelligence for command and control
Containerized Distributed Value-Based Multi-Agent Reinforcement Learning
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism
Leveraging Transformers for StarCraft Macromanagement Prediction
TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations
Applying supervised and reinforcement learning methods to create neural-network-based agents for playing StarCraft II
An Approach to Partial Observability in Games: Learning to Both Act and Observe
Rethinking of AlphaStar
Perceiver IO: A General Architecture for Structured Inputs & Outputs
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning
Context-Aware Sparse Deep Coordination Graphs
Celebrating Diversity in Shared Multi-Agent Reinforcement Learning
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning
Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment
Multi-Agent Deep Reinforcement Learning using Attentive Graph Neural Architectures for Real-Time Strategy Games
Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning
Deep Convolution for Irregularly Sampled Temporal Point Clouds
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement Learning
An Introduction of mini-AlphaStar
NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning
Regularized Softmax Deep Multi-Agent $Q$-Learning
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.