TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems.
Matteo GalliciMario MartinIvan MasmitjaPublished in: CoRR (2023)
Keyphrases
- graph structure
- reinforcement learning problems
- multi agent
- reinforcement learning
- reinforcement learning algorithms
- reinforcement learning methods
- graphical models
- function approximation
- function approximators
- action space
- single agent
- graph structures
- markov decision processes
- markov decision problems
- policy iteration
- state space
- directed graph
- model free
- multiagent reinforcement learning
- temporal difference
- multiple agents
- multi agent systems
- tree structure
- undirected graph
- markov chain
- learning algorithm
- dynamic programming
- real valued
- learning tasks
- optimal policy