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Found 6 papers, 1 papers with code
FM3Q: Factorized Multi-Agent MiniMax Q-Learning for Two-Team Zero-Sum Markov Game
Based on it, we present a novel multi-agent reinforcement learning framework, Factorized Multi-Agent MiniMax Q-Learning (FM3Q), which can factorize the joint minimax Q function into individual ones and iteratively solve for the IGMM-satisfied minimax Q functions for 2t0sMGs.
A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combat
Then the inner loop tracks the macro behavior with a flight controller by calculating the actual input signals for the aircraft.
Event-Triggered Multi-agent Reinforcement Learning with Communication under Limited-bandwidth Constraint
Then the design of the event-triggered strategy is formulated as a constrained Markov decision problem, and reinforcement learning finds the best communication protocol that satisfies the limited bandwidth constraint.
Multiagent Systems
Enhanced Rolling Horizon Evolution Algorithm with Opponent Model Learning: Results for the Fighting Game AI Competition
In contrast to conventional RHEA, an opponent model is proposed and is optimized by supervised learning with cross-entropy and reinforcement learning with policy gradient and Q-learning respectively, based on history observations from opponent.
A Survey of Deep Reinforcement Learning in Video Games
In this paper, we survey the progress of DRL methods, including value-based, policy gradient, and model-based algorithms, and compare their main techniques and properties.
StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning
With reinforcement learning and curriculum transfer learning, our units are able to learn appropriate strategies in StarCraft micromanagement scenarios.
