arXiv:2405.16668v2 Announce Type: replace Abstract: Adversarial Imitation Learning (AIL) faces challenges with sample inefficiency because of its reliance on sufficient on-policy data to evaluate the performance of the current policy during reward function updates. In this work, we study the convergence properties and sample complexity of off-policy AIL algorithms. We show that, even in the absence of importance sampling correction, reusing samples generated by the $o(\sqrt{K})$ most recent policies, where $K$ is the number of iterations of policy updates and reward updates, does not undermine
This research addresses a known challenge in Adversarial Imitation Learning (AIL) regarding sample inefficiency, driven by the ongoing pursuit of more robust and efficient AI training methods.
Improved sample efficiency and convergence guarantees for off-policy AIL could accelerate the development of complex AI agents that learn from expert demonstrations more effectively.
The ability to reuse samples from past policies without undermining convergence enhances the practical applicability of AIL, potentially lowering computational costs and data requirements.
- · AI researchers
- · Developers of autonomous systems
- · Industries deploying AI agents
More efficient training methods for AI models become available.
This could lead to faster deployment cycles for AI agents in various applications, from robotics to enterprise automation.
Reduced resource constraints might democratize advanced AI development, expanding the pool of innovators.
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Read at arXiv cs.LG