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arXiv · 2101.02308

Coding for Distributed Multi-Agent Reinforcement Learning

Baoqian Wang·Junfei Xie·Nikolay Atanasov
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Abstract

This paper aims to mitigate straggler effects in synchronous distributed learning for multi-agent reinforcement learning (MARL) problems. Stragglers arise frequently in a distributed learning system, due to the existence of various system disturbances such as slow-downs or failures of compute nodes and communication bottlenecks. To resolve this issue, we propose a coded distributed learning framework, which speeds up the training of MARL algorithms in the presence of stragglers, while maintaining the same accuracy as the centralized approach. As an illustration, a coded distributed version of the multi-agent deep deterministic policy gradient(MADDPG) algorithm is developed and evaluated. Different coding schemes, including maximum distance separable (MDS)code, random sparse code, replication-based code, and regular low density parity check (LDPC) code are also investigated. Simulations in several multi-robot problems demonstrate the promising performance of the proposed framework.

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Baoqian Wang, Junfei Xie, Nikolay Atanasov. 2021-01-07. Coding for Distributed Multi-Agent Reinforcement Learning. https://arxiv.org/abs/2101.02308

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