Kernekoncepter
The core message of this paper is to develop a distributed and scalable reinforcement learning framework for large-scale cooperative multi-agent systems by leveraging the structures of graphs involved in the problem, including state graph, observation graph, reward graph, and communication graph. The proposed approach constructs local value functions for each agent that can effectively capture the global objective while significantly reducing the sample complexity and computational complexity compared to centralized and consensus-based distributed RL algorithms.
Resumé
The paper proposes a general distributed framework for sample-efficient cooperative multi-agent reinforcement learning (MARL) by utilizing the structures of graphs involved in the problem. It introduces three coupling graphs - state graph, observation graph, and reward graph - to characterize different types of inter-agent couplings. Based on these graphs, the paper derives a learning graph that describes the required information flow during the RL process.
The paper then designs two distributed RL approaches based on local value functions (LVFs) derived from the coupling graphs. The first approach can significantly reduce sample complexity under specific conditions on the graphs. The second approach provides an approximate solution and can be efficient even for problems with dense coupling graphs, by introducing a truncated LVF (TLVF) that involves fewer agents than the original LVF.
The key highlights of the paper are:
- It considers a general MARL formulation with three types of inter-agent couplings (state, observation, and reward) simultaneously, which is more comprehensive than existing works.
- It constructs LVFs for each agent such that the gradient of the LVF w.r.t. the local policy parameter is exactly the same as that of the global value function, enabling efficient policy gradient algorithms.
- It designs a distributed RL algorithm based on local consensus, whose computational complexity depends on the structures of the coupling graphs.
- It introduces TLVFs to handle dense coupling graphs, providing a trade-off between minimizing approximation error and reducing computational complexity.
- The proposed approaches exhibit significantly improved scalability to large-scale multi-agent systems compared to centralized and consensus-based distributed RL algorithms.
Statistik
The paper does not provide any explicit numerical data or statistics. The key results are presented in the form of theoretical analysis and lemmas.
Citater
The paper does not contain any striking quotes that support the key logics.