ConDT leverages an enhanced contrastive loss to train a return-dependent transformation of the input embeddings, which we empirically show clusters these ...
Sep 10, 2022 · We present ConDT, a neural architecture for reinforcement learning that empirically outperforms prior work with a novel approach to learning ...
We accomplish this process by introducing Contrastive Decision Transformers (ConDT), which enables learning discriminable input-space embeddings for DT by ...
We build our Atari implementation on top of minGPT and the original decision transformer and benchmark our results on the DQN-replay dataset.
Duration: 0:51
Posted: Dec 2, 2022
Posted: Dec 2, 2022
Mar 27, 2024 · In this work, we investigate the use of a transformer model as a projection head within the CL framework, aiming to exploit the transformer's ...
Missing: Decision | Show results with:Decision
Aug 15, 2024 · Useful for learning underlying data representations without any explicit labels, contrastive learning comes with numerous real-world use cases; ...
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Aug 31, 2023 · We proposed Multi-objective Decision Transformer (MO-DT), a Decision Transformer variant that effectively harnesses ... Contrastive decision ...
Abstract—Decision Transformers (DTs) have been highly effective for offline reinforcement learning (RL) tasks, suc- cessfully modeling the sequences of ...
In the present study, we propose a novel deep learning model named Hierarchical graph transformEr with contrAstive Learning (HEAL) for protein function ...