Coding for Distributed Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2101.02308v1
• Date: Thu, 7 Jan 2021 00:22:34 GMT
• Title: Coding for Distributed Multi-Agent Reinforcement Learning
• Authors: Baoqian Wang, Junfei Xie, Nikolay Atanasov
• Abstract summary: Stragglers arise frequently in a distributed learning system, due to the existence of various system disturbances. We propose a coded distributed learning framework, which speeds up the training of MARL algorithms in the presence of stragglers. 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.
• Score: 12.366967700730449
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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