  In reinforcement learning, an agent learns to reach a set of goals by means
of an external reward signal. In the natural world, intelligent organisms learn
from internal drives, bypassing the need for external signals, which is
beneficial for a wide range of tasks. Motivated by this observation, we propose
to formulate an intrinsic objective as the mutual information between the goal
states and the controllable states. This objective encourages the agent to take
control of its environment. Subsequently, we derive a surrogate objective of
the proposed reward function, which can be optimized efficiently. Lastly, we
evaluate the developed framework in different robotic manipulation and
navigation tasks and demonstrate the efficacy of our approach. A video showing
experimental results is available at https://youtu.be/CT4CKMWBYz0
