The transition dynamics simply mixes an action and a random sampled latent. It then applies an exponential moving average for temporal persistency, the resulting latent is decoded to image using pretrained generator.

Wesuggestanoveldistributional framework, able to represent arbitrary distribution functions over the continuous actionspace.

We usethisideatoreinterpret aprior RLmethod asperforming contrastivelearning, and then use the idea to propose a much simpler method that achieves similar performance. Across arange ofgoal-conditioned RLtasks, wedemonstrate that contrastive RL methods achieve higher success rates than prior non-contrastive methods, including in the offline RL setting.

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