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Found 4 papers, 2 papers with code
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning
We introduce hybrid execution in multi-agent reinforcement learning (MARL), a new paradigm in which agents aim to successfully perform cooperative tasks with any communication level at execution time by taking advantage of information-sharing among the agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Hierarchically Structured Scheduling and Execution of Tasks in a Multi-Agent Environment
In this work, we set ourselves on a problem that presents itself with a hierarchical structure: the task-scheduling, by a centralised agent, in a dynamic warehouse multi-agent environment and the execution of one such schedule, by decentralised agents with only partial observability thereof.
The Impact of Data Distribution on Q-learning with Function Approximation
We provide a unified theoretical and empirical analysis as to how different properties of the data distribution influence the performance of Q-learning-based algorithms.
CHARET: Character-centered Approach to Emotion Tracking in Stories
Autonomous agents that can engage in social interactions witha human is the ultimate goal of a myriad of applications.
