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To further exploit the redundancy and improve sampling efficiency, we take a different approach, wherein a deep SAR imaging algorithm is proposed.
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We then train two DRL agents, based on Double Deep Q-Network and Deep Sarsa, to generate realistic adversarial samples: the goal is achieving misclassifications by performing small modifications to the initial malicious samples.
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by Jaydeep Sarangi and Anurima Chanda.
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In this paper, we compare several reinforcement learning (Q-Learning, SARSA) and deep reinforcement learning (Deep Q-Network, Deep Sarsa) methods for a task aimed at achieving a goal using robotics arm UR3.
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Subsequent modification provided quick access to an array of compounds, allowed for deep SAR insights, and enabled to optimize the hit scaffold into a lead structure of nanomolar potency combined with favorable in vitro ADME/T features.
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In this paper, the Deep SARSA method is used to find the path of the robot on 5 × 5 environment with the presence of moving obstacles.
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Rajdeep Sardesai, ‘2014: The Election that Changed India’.
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Sharankuma Limbale and Jaydeep Sarangi (Eds.
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