Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning
This addresses scalability issues for real-world applications with many agents, though it is incremental as it builds on existing parameter-sharing techniques.
The paper tackles the scalability problem in multi-agent reinforcement learning by proposing a method that uses structured pruning to enhance the representational capacity of joint policies without adding parameters, achieving significant performance improvements over other parameter-sharing methods in benchmark tasks.
Handling the problem of scalability is one of the essential issues for multi-agent reinforcement learning (MARL) algorithms to be applied to real-world problems typically involving massively many agents. For this, parameter sharing across multiple agents has widely been used since it reduces the training time by decreasing the number of parameters and increasing the sample efficiency. However, using the same parameters across agents limits the representational capacity of the joint policy and consequently, the performance can be degraded in multi-agent tasks that require different behaviors for different agents. In this paper, we propose a simple method that adopts structured pruning for a deep neural network to increase the representational capacity of the joint policy without introducing additional parameters. We evaluate the proposed method on several benchmark tasks, and numerical results show that the proposed method significantly outperforms other parameter-sharing methods.