  Learning good feature representations is important for deep reinforcement
learning (RL). However, with limited experience, RL often suffers from data
inefficiency for training. For un-experienced or less-experienced trajectories
(i.e., state-action sequences), the lack of data limits the use of them for
better feature learning. In this work, we propose a novel method, dubbed
PlayVirtual, which augments cycle-consistent virtual trajectories to enhance
the data efficiency for RL feature representation learning. Specifically,
PlayVirtual predicts future states in the latent space based on the current
state and action by a dynamics model and then predicts the previous states by a
backward dynamics model, which forms a trajectory cycle. Based on this, we
augment the actions to generate a large amount of virtual state-action
trajectories. Being free of groudtruth state supervision, we enforce a
trajectory to meet the cycle consistency constraint, which can significantly
enhance the data efficiency. We validate the effectiveness of our designs on
the Atari and DeepMind Control Suite benchmarks. Our method achieves the
state-of-the-art performance on both benchmarks.
