Researchers introduced a new information removal method for reinforcement learning policies that uses a learned masking function integrated into attention weights of a policy network, improving generalization to unseen tasks. This approach outperforms traditional methods on the Procgen benchmark, offering content creators and researchers a more reliable way to enhance policy adaptability in diverse environments.
Read the full article at arXiv cs.LG (ML)
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Ali NematiWritten by Ali
