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Methods for Efficient Deep Reinforcement Learning

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Figure 2.4: Reinforcement learning taxonomy of approaches. Value-based methods learn the long-term results of states or state-action pairs
Figure 2.5: Example of our custom AirSim environment. After the environment is reset, cubes are placed randomly in front of the drone
Table 3.1: Energy and die area costs for various operations (45nm) [31]. Quantized operators and operands are preferred for low-power and low-resource applications
Table 3.2: Number and cost of parameters and MACs for popular deep neural network architectures
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