  Deep reinforcement learning for multi-agent cooperation and competition has
been a hot topic recently. This paper focuses on cooperative multi-agent
problem based on actor-critic methods under local observations settings. Multi
agent deep deterministic policy gradient obtained state of art results for some
multi-agent games, whereas, it cannot scale well with growing amount of agents.
In order to boost scalability, we propose a parameter sharing deterministic
policy gradient method with three variants based on neural networks, including
actor-critic sharing, actor sharing and actor sharing with partially shared
critic. Benchmarks from rllab show that the proposed method has advantages in
learning speed and memory efficiency, well scales with growing amount of
agents, and moreover, it can make full use of reward sharing and
exchangeability if possible.
