An Analysis of Streaming Deep Reinforcement Learning for Adaptive Continual Learning in Robotics
Bridging the gap between static training and real-world environmental adaptability in robotics.
Over the course of a lifetime, robots may encounter novel scenarios unaccounted for in its original training that result in performance degradation. One common approach to mitigating this issue is to further grow the offline training dataset in hopes of producing a policy robust ...
ArXiv CS.RO
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Streaming deep reinforcement learning enables robots to evolve their operational capabilities in real-time without the bottleneck of recurring offline training cycles. This shift is critical for deploying autonomous agents into dynamic environments where unforeseen variables previously caused systemic performance failure.
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