  Exploration is a difficult challenge in reinforcement learning and is of
prime importance in sparse reward environments. However, many of the state of
the art deep reinforcement learning algorithms, that rely on epsilon-greedy,
fail on these environments. In such cases, empowerment can serve as an
intrinsic reward signal to enable the agent to maximize the influence it has
over the near future. We formulate empowerment as the channel capacity between
states and actions and is calculated by estimating the mutual information
between the actions and the following states. The mutual information is
estimated using Mutual Information Neural Estimator and a forward dynamics
model. We demonstrate that an empowerment driven agent is able to improve
significantly the score of a baseline DQN agent on the game of Montezuma's
Revenge.
