Value Explicit Pretraining (VEP) introduces a method to learn transferable representations for reinforcement learning, outperforming current methods on Atari and visual navigation tasks. By leveraging Monte Carlo estimates of control heuristics, VEP achieves significant improvements in rewards and sample efficiency.
The content discusses the challenges in visual representation learning and the importance of discovering correct inductive biases. Various pretraining methods are compared, highlighting the need for approaches that encode information useful for downstream control during representation learning.
VEP utilizes offline demonstration datasets with reward labels to learn representations based on similar Monte Carlo Bellman return estimates across tasks. This allows for efficient policy learning and transfer to new tasks with related goals.
Experiments on Atari games and visual navigation benchmarks demonstrate the effectiveness of VEP, showcasing superior performance compared to state-of-the-art methods. The method's implementation details, including contrastive representation learning and discounted returns, are thoroughly explained.
Overall, VEP presents a promising approach to address the challenge of transferring policies to novel but related tasks efficiently through learned representations based on value estimates.
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by Kiran Lekkal... at arxiv.org 03-08-2024
https://arxiv.org/pdf/2312.12339.pdfDeeper Inquiries