A saliency-weighted quantization-aware training method lets 4-bit quantized imitation-learning policies match full-precision success rates across robot manipulation, driving, and control benchmarks.
Low Precision Policy Distillation with Application to Low-Power, Real-time Sensation-Cognition-Action Loop with Neuromorphic Computing
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abstract
Low precision networks in the reinforcement learning (RL) setting are relatively unexplored because of the limitations of binary activations for function approximation. Here, in the discrete action ATARI domain, we demonstrate, for the first time, that low precision policy distillation from a high precision network provides a principled, practical way to train an RL agent. As an application, on 10 different ATARI games, we demonstrate real-time end-to-end game playing on low-power neuromorphic hardware by converting a sequence of game frames into discrete actions.
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Saliency-Aware Quantized Imitation Learning for Efficient Robotic Control
A saliency-weighted quantization-aware training method lets 4-bit quantized imitation-learning policies match full-precision success rates across robot manipulation, driving, and control benchmarks.