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Rishav Rishav

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Handling Delay in Real-Time Reinforcement Learning
Ivan Anokin
Rishav Rishav
Matthew Riemer
Stephen Chung
Irina Rish
Samira Ebrahimi Kahou
Real-time reinforcement learning (RL) introduces several challenges. First, policies are constrained to a fixed number of actions per second… (voir plus) due to hardware limitations. Second, the environment may change while the network is still computing an action, leading to observational delay. The first issue can partly be addressed with pipelining, leading to higher throughput and potentially better policies. However, the second issue remains: if each neuron operates in parallel with an execution time of
2025-01-21
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net