AI Insight
This research introduces Variance Reduction Experience Replay (VRER), a new framework for reinforcement learning that improves sample efficiency by selectively reusing the most informative past observations rather than treating all historical data equally. The authors provide theoretical convergence guarantees and demonstrate that their approach identifies a fundamental trade-off where reusing older samples reduces variance in policy gradient estimates but may increase estimation bias. Experimental results show VRER accelerates learning and outperforms existing state-of-the-art policy optimization algorithms.
Why it matters
This work could significantly improve the efficiency of training AI systems that learn through trial and error, potentially reducing the computational costs and time required to develop effective policies for complex real-world applications like robotics, autonomous systems, and resource management.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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Abstract: Effective reinforcement learning (RL) for complex stochastic systems requires leveraging historical data to improve sample efficiency and accelerate policy optimization. However, classical experience replay treats all past observations uniformly and fails to account for their varying contributions to learning. To address this limitation, we propose Variance Reduction Experience Replay (VRER), a principled framework that selectively reuses informative samples to reduce the variance of policy gradient estimates. VRER is algorithm-agnostic and can be integrated with existing policy optimization methods, yielding the sample-efficient off-policy algorithm, Policy Gradient with VRER (PG-VRER). To provide rigorous theoretical guarantees, we develop a novel analysis framework for experience replay that explicitly accounts for dependencies induced by Markovian dynamics and behavior-policy interactions. Using this framework, we establish finite-time convergence guarantees for PG-VRER and characterize a fundamental bias-variance trade-off: reusing older samples reduces gradient variance but may introduce greater estimation bias. Extensive experiments show that VRER consistently accelerates learning and outperforms state-of-the-art policy optimization algorithms
Source: Variance Reduction Based Experience Replay for Policy Optimization