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Continual learning is the ability of agents to improve their capacitiesthroughout multiple tasks continually. While recent works in the literature ofcontinual learning mostly focused on developing either particular lossfunctions or specialized structures of neural network explaining the episodicmemory or neural plasticity, we study continual learning from the perspectiveof the training mechanism. Specifically, we propose a COnitnual Match BAsedTraining (COMBAT) framework for training a population of advantage-actor-critic(A2C) agents in Pommerman, a partially observable multi-agent environment withno communication. Following the COMBAT framework, we trained an agent, namely,Navocado, that won the title of the top 1 learning agent in the NeurIPS 2018Pommerman Competition. Two critical features of our agent are worth mentioning.Firstly, our agent did not learn from any demonstrations. Secondly, our agentis highly reproducible. As a technical report, we articulate the design ofstate space, action space, reward, and most importantly, the COMBAT frameworkfor our Pommerman agent. We show in the experiments that Pommerman is a perfectenvironment for studying continual learning, and the agent can improve itsperformance by continually learning new skills without forgetting the old ones.Finally, the result in the Pommerman Competition verifies the robustness of ouragent when competing with various opponents.

Continual Match Based Training in Pommerman: Technical Report