Consolidation via Policy Information Regularization in Deep RL for
  Multi-Agent Games
  This paper introduces an information-theoretic constraint on learned policy
complexity in the Multi-Agent Deep Deterministic Policy Gradient (MADDPG)
reinforcement learning algorithm. Previous research with a related approach in
continuous control experiments suggests that this method favors learning
policies that are more robust to changing environment dynamics. The multi-agent
game setting naturally requires this type of robustness, as other agents'
policies change throughout learning, introducing a nonstationary environment.
For this reason, recent methods in continual learning are compared to our
approach, termed Capacity-Limited MADDPG. Results from experimentation in
multi-agent cooperative and competitive tasks demonstrate that the
capacity-limited approach is a good candidate for improving learning
performance in these environments.
