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Found 4 papers, 2 papers with code
Scalable Multi-Agent Model-Based Reinforcement Learning
While in mixed environments full autonomy of the agents can be a desirable outcome, cooperative environments allow agents to share information to facilitate coordination.
Model-based Reinforcement Learning
reinforcement-learning
+2
Self-Imitation Learning from Demonstrations
Modern LfD algorithms require meticulous tuning of hyperparameters that control the influence of demonstrations and, as we show in the paper, struggle with learning from suboptimal demonstrations.
Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World
However, the coordination of hundreds of agents in a real-life setting like a railway network remains challenging and the Flatland environment used for the competition models these real-world properties in a simplified manner.
Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive Environments
Recent reinforcement learning studies extensively explore the interplay between cooperative and competitive behaviour in mixed environments.
