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Found 6 papers, 2 papers with code
PettingZoo: Gym for Multi-Agent Reinforcement Learning
This paper introduces the PettingZoo library and the accompanying Agent Environment Cycle ("AEC") games model.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Agent Environment Cycle Games
Partially Observable Stochastic Games (POSGs) are the most general and common model of games used in Multi-Agent Reinforcement Learning (MARL).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Stochastic Optimization and Learning for Two-Stage Supplier Problems
The main focus of this paper is radius-based (supplier) clustering in the two-stage stochastic setting with recourse, where the inherent stochasticity of the model comes in the form of a budget constraint.
Data Structures and Algorithms
Multi-Agent Informational Learning Processes
We introduce a new mathematical model of multi-agent reinforcement learning, the Multi-Agent Informational Learning Processor "MAILP" model.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Revisiting Parameter Sharing in Multi-Agent Deep Reinforcement Learning
Next, we formally introduce methods to extend parameter sharing to learning in heterogeneous observation and action spaces, and prove that these methods allow for convergence to optimal policies.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Scenario Submodular Cover
In contrast, in Stochastic Submodular Cover, the variables of the input distribution are assumed to be independent, and the distribution of each variable is given as input.
