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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 201–250 of 311 papers

TitleStatusHype
Revisiting the Monotonicity Constraint in Cooperative Multi-Agent Reinforcement Learning—0
Disentangling Sources of Risk for Distributional Multi-Agent Reinforcement Learning—0
Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents—0
Adversary agent reinforcement learning for pursuit-evasion—0
An Approach to Partial Observability in Games: Learning to Both Act and Observe—0
Cooperative Exploration for Multi-Agent Deep Reinforcement Learning—0
Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi—0
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning—0
Truthful Self-Play—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
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients—0
Optimize Neural Fictitious Self-Play in Regret Minimization Thinking—0
NQMIX: Non-monotonic Value Function Factorization for Deep Multi-Agent Reinforcement Learning—0
"Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us?—0
Regularized Softmax Deep Multi-Agent Q-Learning—0
Meta-Learning for Planning: Automatic Synthesis of Sample Based Planners—0
Reinforcement Learning of Implicit and Explicit Control Flow in Instructions—0
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents—0
Disturbing Reinforcement Learning Agents with Corrupted Rewards—0
Factored Action Spaces in Deep Reinforcement Learning—0
Coordinated Multi-Agent Exploration Using Shared Goals—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
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms—0
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II—0
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement LearningCode0
Exact Reduction of Huge Action Spaces in General Reinforcement Learning—0
Reinforcement Learning for the Beginning of Starcraft II Game—0
Multi-agent Policy Optimization with Approximatively Synchronous Advantage Estimation—0
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning—0
Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning—0
AI and Wargaming—0
QR-MIX: Distributional Value Function Factorisation for Cooperative Multi-Agent Reinforcement Learning—0
BGC: Multi-Agent Group Belief with Graph Clustering—0
Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection—0
Improving Multi-Agent Cooperation using Theory of Mind—0
Artificial Intelligence is stupid and causal reasoning won't fix it—0
QTRAN++: Improved Value Transformation for Cooperative Multi-Agent Reinforcement Learning—0
Lifelong Learning using Eigentasks: Task Separation, Skill Acquisition and Selective Transfer—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
The Adversarial Resilience Learning Architecture for AI-based Modelling, Exploration, and Operation of Complex Cyber-Physical Systems—0
Optimal Any-Angle Pathfinding on a Sphere—0
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