Appending autoencoder-derived trajectory encodings to the state space improves offline RL transfer to new CartPole dynamics compared with BCQ, though the effect is small in some environments.
Fleet control using coregionalized gaussian process policy iteration
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TEA: Trajectory Encoding Augmentation for Robust and Transferable Policies in Offline Reinforcement Learning
Appending autoencoder-derived trajectory encodings to the state space improves offline RL transfer to new CartPole dynamics compared with BCQ, though the effect is small in some environments.