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Deep reinforcement learning (DRL) is one of the most powerful tools forsynthesizing complex robotic behaviors. But training DRL models is incrediblycompute and memory intensive, requiring large training datasets and replaybuffers to achieve performant results. This poses a challenge for the nextgeneration of field robots that will need to learn on the edge to adapt totheir environment. In this paper, we begin to address this issue throughobservation space quantization. We evaluate our approach using four simulatedrobot locomotion tasks and two state-of-the-art DRL algorithms, the on-policyProximal Policy Optimization (PPO) and off-policy Soft Actor-Critic (SAC) andfind that observation space quantization reduces overall memory costs by asmuch as 4.2x without impacting learning performance.

Just Round: Quantized Observation Spaces Enable Memory Efficient Learning of Dynamic Locomotion