Object-oriented representations in reinforcement learning have shown promise in transfer learning, with previous research introducing a propositional objectoriented framework that has provably efficient learning bounds with respect to samplecomplexity.

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.

Weprovide aunified analysis for MobILE, and demonstrate that MobILE enjoys strong performance guarantees for classes of MDP dynamics that satisfy certain well studied notions of structural complexity. We also show that the ILFO problem isstrictly harder than the standard IL problem by presenting an exponential sample complexity separation between ILand ILFO.

To extend this and adapt other results to our setting, we could now apply the Simulation Lemma [1]to bound the value difference given the model error,or alternatively, develop the theory in the direction of[55]andrelated work. Code is available at https://github.com/spitis/mocoda Forexample, in2d Navigation,themaskfunction was implementedasfollows: def Mask2dNavigation(input_tensor): """ accepts B x num_sa_features, and returns B x num_parents x num_children """ # base local mask mask = torch.tensor( Theadvantageofthisapproach isthat we can easily do conditional sampling incase of overlapping parent sets. The CQL implementation uses SAC [17].