to this essential role, ADC circuits have been well studied over 40 years, and many problems associated with them have already been solved. Then, we discuss two formulations of meta learning. EndNote citation: 0 Thesis, a Duan, Yida, t Design Techniques for Ultra-High-Speed christopher columbus letters essay Time-Interleaved Analog-to-Digital Converters (ADCs). The second formulation is meta learning for imitation learning, where the task is specified through an expert demonstration of the task, and the agent needs to mimic the behavior of the expert to achieve good performance under new situations of the same task, as measured. We begin with a thorough review of existing policy learning algorithms for control, which motivates the need for better algorithms that can solve complicated tasks with affordable sample complexity. An extensive statistical analysis is provided to verify the correction algorithm can greatly reduce sparkle-code error-rates. We also analyze their current limitations, including challenges associated with long horizons and imperfect demonstrations, which suggest important venues for future work. Advisor: Pieter Abbeel, bibTeX citation: @phdthesisDuan:eecs-2017-233, Author Duan, Rocky, Title Meta Learning for Control, School eecs Department, University of California, Berkeley, Year 2017, Month Dec, URL ml, Number UCB/eecs-2017-233, Abstract In this thesis, we discuss meta learning for control: policy learning algorithms that can themselves.
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D 2017 8 May 1 @ UCB/eecs-2017-10, u ml, f Duan:eecs-2017-10. Several recent works has demonstrated success in achieving high sampling rate. In this thesis, I will first propose a new cascode-based T H circuits to improve the ADC bandwidth beyond the limit of conventional switch-based T H circuits. Yida Duan, eECS Department, university of California, Berkeley, technical Report. The first formulation is meta learning for reinforcement learning, where the task is specified through a reward function, and the agent needs to improve its performance by acting in the environment, receiving scalar reward signals, and adjusting its strategy according to the information it receives. Then, a system design and optimization methodology of hierarchical time-interleaved sampling network is presented in the context of cascode. Furthermore, asynchronous SAR sub-ADCs are often used in these designs to push the sampling rate even further.