  We describe our solution approach for Pommerman TeamRadio, a competition
environment associated with NeurIPS 2019. The defining feature of our algorithm
is achieving sample efficiency within a restrictive computational budget while
beating the previous years learning agents. The proposed algorithm (i) uses
imitation learning to seed the policy, (ii) explicitly defines the
communication protocol between the two teammates, (iii) shapes the reward to
provide a richer feedback signal to each agent during training and (iv) uses
masking for catastrophic bad actions. We describe extensive tests against
baselines, including those from the 2019 competition leaderboard, and also a
specific investigation of the learned policy and the effect of each
modification on performance. We show that the proposed approach is able to
achieve competitive performance within half a million games of training,
significantly faster than other studies in the literature.
