Actor-Free Continuous Control via Structurally Maximizable Q-Functions

• URL: http://arxiv.org/abs/2510.18828v1
• Date: Tue, 21 Oct 2025 17:24:27 GMT
• Title: Actor-Free Continuous Control via Structurally Maximizable Q-Functions
• Authors: Yigit Korkmaz, Urvi Bhuwania, Ayush Jain, Erdem Bıyık,
• Abstract summary: We propose a purely value-based framework for continuous control that revisits structural of Q-functions.<n>We evaluate the proposed actor-free Q-learning approach on a range of standard simulation tasks.
• Score: 3.7193386971098406
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Value-based algorithms are a cornerstone of off-policy reinforcement learning due to their simplicity and training stability. However, their use has traditionally been restricted to discrete action spaces, as they rely on estimating Q-values for individual state-action pairs. In continuous action spaces, evaluating the Q-value over the entire action space becomes computationally infeasible. To address this, actor-critic methods are typically employed, where a critic is trained on off-policy data to estimate Q-values, and an actor is trained to maximize the critic's output. Despite their popularity, these methods often suffer from instability during training. In this work, we propose a purely value-based framework for continuous control that revisits structural maximization of Q-functions, introducing a set of key architectural and algorithmic choices to enable efficient and stable learning. We evaluate the proposed actor-free Q-learning approach on a range of standard simulation tasks, demonstrating performance and sample efficiency on par with state-of-the-art baselines, without the cost of learning a separate actor. Particularly, in environments with constrained action spaces, where the value functions are typically non-smooth, our method with structural maximization outperforms traditional actor-critic methods with gradient-based maximization. We have released our code at https://github.com/USC-Lira/Q3C.

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