  Learning representations for reinforcement learning (RL) has shown much
promise for continuous control. We propose an efficient representation learning
method using only a self-supervised latent-state consistency loss. Our approach
employs an encoder and a dynamics model to map observations to latent states
and predict future latent states, respectively. We achieve high performance and
prevent representation collapse by quantizing the latent representation such
that the rank of the representation is empirically preserved. Our method, named
iQRL: implicitly Quantized Reinforcement Learning, is straightforward,
compatible with any model-free RL algorithm, and demonstrates excellent
performance by outperforming other recently proposed representation learning
methods in continuous control benchmarks from DeepMind Control Suite.
