  One of the fundamental challenges in reinforcement learning (RL) is the one
of data efficiency: modern algorithms require a very large number of training
samples, especially compared to humans, for solving environments with
high-dimensional observations. The severity of this problem is increased when
the reward signal is sparse. In this work, we propose learning a state
representation in a self-supervised manner for reward prediction. The reward
predictor learns to estimate either a raw or a smoothed version of the true
reward signal in environment with a single, terminating, goal state. We augment
the training of out-of-the-box RL agents by shaping the reward using our reward
predictor during policy learning. Using our representation for preprocessing
high-dimensional observations, as well as using the predictor for reward
shaping, is shown to significantly enhance Actor Critic using
Kronecker-factored Trust Region and Proximal Policy Optimization in single-goal
environments with visual inputs.
