  The Pommerman Team Environment is a recently proposed benchmark which
involves a multi-agent domain with challenges such as partial observability,
decentralized execution (without communication), and very sparse and delayed
rewards. The inaugural Pommerman Team Competition held at NeurIPS 2018 hosted
25 participants who submitted a team of 2 agents. Our submission
nn_team_skynet955_skynet955 won 2nd place of the "learning agents'' category.
Our team is composed of 2 neural networks trained with state of the art deep
reinforcement learning algorithms and makes use of concepts like reward
shaping, curriculum learning, and an automatic reasoning module for action
pruning. Here, we describe these elements and additionally we present a
collection of open-sourced agents that can be used for training and testing in
the Pommerman environment. Code available at:
https://github.com/BorealisAI/pommerman-baseline
