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Found 4 papers, 1 papers with code
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning
We investigate the use of natural language to drive the generalization of policies in multi-agent settings.
Multi-agent Reinforcement Learning
reinforcement-learning
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WILD-SCAV: Benchmarking FPS Gaming AI on Unity3D-based Environments
It provides realistic 3D environments of variable complexity, various tasks, and multiple modes of interaction, where agents can learn to perceive 3D environments, navigate and plan, compete and cooperate in a human-like manner.
MA2QL: A Minimalist Approach to Fully Decentralized Multi-Agent Reinforcement Learning
Decentralized learning has shown great promise for cooperative multi-agent reinforcement learning (MARL).
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II
In this paper, we will share the key insights and optimizations on efficient imitation learning and reinforcement learning for StarCraft II full game.
