arXiv:2605.23182v1 Announce Type: new Abstract: Pure exploration in episodic Reinforcement Learning has primarily focused on Best Policy Identification (BPI), which seeks to identify a (near)-optimal policy with high confidence. Motivated by practical settings where a ``good enough'' policy suffices, we study an alternate objective of Good Policy Identification (GPI). For a given reward threshold $\mu_0$, GPI only requires identifying a policy with expected reward in an episode at least $\mu_0$ if such a policy exists (positive instance), or declaring None if no such policy exists (negative in
This research addresses a growing need for more efficient and practical AI development, especially as real-world applications demand 'good enough' solutions rather than purely optimal ones.
A strategic reader should care because improving the efficiency of policy identification in Reinforcement Learning can accelerate AI deployment and reduce computational costs in various applications.
This paper redefines a core objective in Reinforcement Learning, potentially shifting research and development focus towards more pragmatic and resource-efficient AI agent training paradigms.
- · AI developers
- · Robotics companies
- · Logistics and automation
- · Edge AI computing
- · Inefficient RL algorithms
- · Developers focused solely on global optimality
Faster development and deployment of AI agents in practical scenarios where optimality is not strictly required.
Reduced computational resource demands for training certain types of AI agents, potentially democratizing access to RL development.
The proliferation of 'good enough' AI solutions leading to more widespread automation in sectors currently bottlenecked by the complexity of achieving optimal performance.
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Read at arXiv cs.LG