Continual Match Based Training in Pommerman: Technical Report
  Continual learning is the ability of agents to improve their capacities
throughout multiple tasks continually. While recent works in the literature of
continual learning mostly focused on developing either particular loss
functions or specialized structures of neural network explaining the episodic
memory or neural plasticity, we study continual learning from the perspective
of the training mechanism. Specifically, we propose a COnitnual Match BAsed
Training (COMBAT) framework for training a population of advantage-actor-critic
(A2C) agents in Pommerman, a partially observable multi-agent environment with
no communication. Following the COMBAT framework, we trained an agent, namely,
Navocado, that won the title of the top 1 learning agent in the NeurIPS 2018
Pommerman Competition. Two critical features of our agent are worth mentioning.
Firstly, our agent did not learn from any demonstrations. Secondly, our agent
is highly reproducible. As a technical report, we articulate the design of
state space, action space, reward, and most importantly, the COMBAT framework
for our Pommerman agent. We show in the experiments that Pommerman is a perfect
environment for studying continual learning, and the agent can improve its
performance by continually learning new skills without forgetting the old ones.
Finally, the result in the Pommerman Competition verifies the robustness of our
agent when competing with various opponents.
