  Despite advancements in deep reinforcement learning algorithms, developing an
effective exploration strategy is still an open problem. Most existing
exploration strategies either are based on simple heuristics, or require the
model of the environment, or train additional deep neural networks to generate
imagination-augmented paths. In this paper, a revolutionary algorithm, called
Policy Augmentation, is introduced. Policy Augmentation is based on a newly
developed inductive matrix completion method. The proposed algorithm augments
the values of unexplored state-action pairs, helping the agent take actions
that will result in high-value returns while the agent is in the early
episodes. Training deep reinforcement learning algorithms with high-value
rollouts leads to the faster convergence of deep reinforcement learning
algorithms. Our experiments show the superior performance of Policy
Augmentation. The code can be found at:
https://github.com/arashmahyari/PolicyAugmentation.
