 VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15 \% to 49\% in DeepMind Control suite benchmark and 61 \% to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode  .
<meaning-changed>  VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15 \% to 49\% in DeepMind Control suite benchmark and 61 \% to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode  .
 VAI empirically shows powerful generalization capabilities and significantly outperforms current state-of-the-art (SOTA) method by 15 \% to 49\% in DeepMind Control suite benchmark and 61 \% to 229\% in our proposed robot manipulation benchmark, in term of cumulative rewards per episode on DeepMind Control (our DrawerWorld Manipulation) benchmarks. Our results demonstrate that it is not only possible to learn domain-invariant vision without any supervision, but freeing RL from visual distractions also makes the policy more focused and thus far better .