  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.
