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SUMMARY:[C4AI] Panayiotis Panayiotou PhD Candidate\, Univ. of Bath
DESCRIPTION:Robust policies enable reinforcement learning agents to effectively adapt to and operate in unpredictable\, dynamic\, and ever-changing real-world environments. Factored representations\, which break down complex state and action spaces into distinct components\, can improve generalization and sample efficiency in policy learning. In this paper\, we explore how the curriculum of an agent using a factored state representation affects the robustness of the learned policy. We experimentally demonstrate three simple curricula\, such as varying only the variable of highest regret between episodes\, that can significantly enhance policy robustness\, offering practical insights for reinforcement learning in complex environments.\n\n------\n\nPowered by addevent.com \nShare your next event with us!\n
X-ALT-DESC;FMTTYPE=text/html:Robust policies enable reinforcement learning agents to effectively adapt to and operate in unpredictable, dynamic, and ever-changing real-world environments. Factored representations, which break down complex state and action spaces into distinct components, can improve generalization and sample efficiency in policy learning. In this paper, we explore how the curriculum of an agent using a factored state representation affects the robustness of the learned policy. We experimentally demonstrate three simple curricula, such as varying only the variable of highest regret between episodes, that can significantly enhance policy robustness, offering practical insights for reinforcement learning in complex environments.<br /><br />------<br /><br />Powered by addevent.com <br>Share your next event with us!<br>
LOCATION:https://meet.google.com/sqj-qimt-yno?hs=122&authuser=0
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