A current examine addresses these issues by suggesting a novel actor-critic algorithm known as Be taught to drive with Digital Reminiscence.
It learns the digital latent setting mannequin from actual interplay information. The digital setting is then predicted, and imagined trajectories are recorded because the digital reminiscence. The coverage is optimized with out the necessity for actual interplay information.
A double critic method makes the method extra secure by decreasing the state worth overestimation, which is brought on by errors and noise. Within the activity of lane-keeping in a roundabout, the urged mannequin achieved extra secure coaching and higher management efficiency than present approaches.
Reinforcement studying has proven nice potential in growing high-level autonomous driving. Nevertheless, for high-dimensional duties, present RL strategies undergo from low information effectivity and oscillation within the coaching course of. This paper proposes an algorithm known as Be taught to drive with Digital Reminiscence (LVM) to beat these issues. LVM compresses the high-dimensional data into compact latent states and learns a latent dynamic mannequin to summarize the agent’s expertise. Varied imagined latent trajectories are generated as digital reminiscence by the latent dynamic mannequin. The coverage is realized by propagating gradient by way of the realized latent mannequin with the imagined latent trajectories and thus results in excessive information effectivity. Moreover, a double critic construction is designed to scale back the oscillation in the course of the coaching course of. The effectiveness of LVM is demonstrated by an image-input autonomous driving activity, during which LVM outperforms the prevailing methodology by way of information effectivity, studying stability, and management efficiency.
Analysis paper: Zhang, Y., “Steadily Be taught to Drive with Digital Reminiscence”, 2021. Hyperlink: https://arxiv.org/abs/2102.08072
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