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Titlebook: Intelligent Systems and Applications; Proceedings of the 2 Kohei Arai,Supriya Kapoor,Rahul Bhatia Conference proceedings 2021 Springer Natu

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發(fā)表于 2025-3-30 12:08:33 | 只看該作者
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Driving Reinforcement Learning with Models,MPC). We introduce an algorithm, the MPC augmented RL (MPRL) that combines RL and MPC in a novel way so that they can augment each other’s strengths. We demonstrate the effectiveness of the MPRL by letting it play against the Atari game Pong. For this task, the results highlight how MPRL is able to
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發(fā)表于 2025-3-30 23:23:14 | 只看該作者
Learning Actions with Symbolic Literals and Continuous Effects for a Waypoint Navigation Simulation must perform locomotion behavior. Our algorithm is capable of learning operator’s symbolic literals and continuous effects even under noisy training data. It accepts as input a set of preprocessed positive and negative simulation-generated examples. It identifies symbolic preconditions using a MAX-
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發(fā)表于 2025-3-31 02:02:29 | 只看該作者
Understanding and Exploiting Dependent Variables with Deep Metric Learning,this space corresponds with a predefined notion of similarity. This paper investigates how the mapping element of DML may be exploited in situations where the salient features in arbitrary classification problems vary over time or due to changing underlying variables. Examples of such variable featu
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