  Transfer Learning has shown great potential to enhance single-agent
Reinforcement Learning (RL) efficiency. Similarly, Multiagent RL (MARL) can
also be accelerated if agents can share knowledge with each other. However, it
remains a problem of how an agent should learn from other agents. In this
paper, we propose a novel Multiagent Policy Transfer Framework (MAPTF) to
improve MARL efficiency. MAPTF learns which agent's policy is the best to reuse
for each agent and when to terminate it by modeling multiagent policy transfer
as the option learning problem. Furthermore, in practice, the option module can
only collect all agent's local experiences for update due to the partial
observability of the environment. While in this setting, each agent's
experience may be inconsistent with each other, which may cause the inaccuracy
and oscillation of the option-value's estimation. Therefore, we propose a novel
option learning algorithm, the successor representation option learning to
solve it by decoupling the environment dynamics from rewards and learning the
option-value under each agent's preference. MAPTF can be easily combined with
existing deep RL and MARL approaches, and experimental results show it
significantly boosts the performance of existing methods in both discrete and
continuous state spaces.
