Abstract:Reinforcement Learning (RL) has presented an impressive performance in video games through raw pixel imaging and continuous control tasks. However, RL performs poorly with high-dimensional observations such as raw pixel images. It is generally accepted that physical state-based RL policies such as laser sensor measurements give a more sampleefficient result than learning by pixels. This work presents a new approach that extracts information from a depth map estimation to teach an RL agent to perform the maples… Show more
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