Deep neural networks have become commonplace in the domain of reinforcement
learning, but are often expensive in terms of the number of parameters needed.
While compressing deep neural networks has of late assumed great importance to
overcome this drawback, little work has been done to address this problem in
the context of reinforcement learning agents. This work aims at making first
steps towards model compression in an RL agent. In particular, we compress
networks to drastically reduce the number of parameters in them (to sizes less
than 3% of their original size), further facilitated by applying a global max
pool after the final convolution layer, and propose using Actor-Mimic in the
context of compression. Finally, we show that this global max-pool allows for
weakly supervised object localization, improving the ability to identify the
agent's points of focus.