arXiv:2512.06244v2 Announce Type: replace Abstract: The exploration-exploitation dilemma in reinforcement learning (RL) is a fundamental challenge to efficient RL algorithms. Existing algorithms for finite state and action discounted RL problems address this by assuming sufficient exploration over both state and action spaces. However, this yields non-implementable algorithms and sub-optimal performance. To resolve these limitations, we introduce a new class of methods with auto-exploration, or methods that automatically explore both state and action spaces. Auto-exploration can be applied in
The continuous drive for more efficient and robust reinforcement learning algorithms pushes research into fundamental challenges like the exploration-exploitation dilemma.
Improved auto-exploration techniques can significantly accelerate the development and reliability of advanced AI systems, particularly for autonomous agents operating in complex, real-world environments.
This research introduces a new class of methods for automatic exploration, potentially resolving a critical limitation in existing RL algorithms by making them more implementable and optimal for state and action space exploration.
- · AI developers
- · Robotics industry
- · Autonomous systems sector
- · Academic researchers in AI
- · Developers relying on sub-optimal RL exploration methods
More efficient and generalizable reinforcement learning models become feasible.
Accelerated deployment of advanced AI applications in areas requiring real-time decision-making and adaptation.
Enhanced AI capabilities could reduce the need for extensive human supervision in complex operational environments, impacting various white-collar workflows.
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Read at arXiv cs.LG