  Reinforcement Learning (RL) is a general framework concerned with an agent
that seeks to maximize rewards in an environment. The learning typically
happens through trial and error using explorative methods, such as
epsilon-greedy. There are two approaches, model-based and model-free
reinforcement learning, that show concrete results in several disciplines.
Model-based RL learns a model of the environment for learning the policy while
model-free approaches are fully explorative and exploitative without
considering the underlying environment dynamics. Model-free RL works
conceptually well in simulated environments, and empirical evidence suggests
that trial and error lead to a near-optimal behavior with enough training. On
the other hand, model-based RL aims to be sample efficient, and studies show
that it requires far less training in the real environment for learning a good
policy.
  A significant challenge with RL is that it relies on a well-defined reward
function to work well for complex environments and such a reward function is
challenging to define. Goal-Directed RL is an alternative method that learns an
intrinsic reward function with emphasis on a few explored trajectories that
reveals the path to the goal state.
  This paper introduces a novel reinforcement learning algorithm for predicting
the distance between two states in a Markov Decision Process. The learned
distance function works as an intrinsic reward that fuels the agent's learning.
Using the distance-metric as a reward, we show that the algorithm performs
comparably to model-free RL while having significantly better
sample-efficiently in several test environments.
