Q-STAC: Q-Guided Stein Variational Model Predictive Actor-Critic

• URL: http://arxiv.org/abs/2507.06625v1
• Date: Wed, 09 Jul 2025 07:53:53 GMT
• Title: Q-STAC: Q-Guided Stein Variational Model Predictive Actor-Critic
• Authors: Shizhe Cai, Jayadeep Jacob, Zeya Yin, Fabio Ramos,
• Abstract summary: This paper introduces the Q-guided STein variational model predictive Actor-Critic (Q-STAC) framework for continuous control tasks.<n>Our method optimize control sequences directly using learned Q-values as objectives, eliminating the need for explicit cost function design.<n>Experiments on 2D navigation and robotic manipulation tasks demonstrate that Q-STAC achieves superior sample efficiency, robustness, and optimality compared to state-of-the-art algorithms.
• Score: 12.837649598521102
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep reinforcement learning has shown remarkable success in continuous control tasks, yet often requires extensive training data, struggles with complex, long-horizon planning, and fails to maintain safety constraints during operation. Meanwhile, Model Predictive Control (MPC) offers explainability and constraint satisfaction, but typically yields only locally optimal solutions and demands careful cost function design. This paper introduces the Q-guided STein variational model predictive Actor-Critic (Q-STAC), a novel framework that bridges these approaches by integrating Bayesian MPC with actor-critic reinforcement learning through constrained Stein Variational Gradient Descent (SVGD). Our method optimizes control sequences directly using learned Q-values as objectives, eliminating the need for explicit cost function design while leveraging known system dynamics to enhance sample efficiency and ensure control signals remain within safe boundaries. Extensive experiments on 2D navigation and robotic manipulation tasks demonstrate that Q-STAC achieves superior sample efficiency, robustness, and optimality compared to state-of-the-art algorithms, while maintaining the high expressiveness of policy distributions. Experiment videos are available on our website: https://sites.google.com/view/q-stac

Related papers

• Q-function Decomposition with Intervention Semantics with Factored Action Spaces [51.01244229483353]
  We consider Q-functions defined over a lower dimensional projected subspace of the original action space, and study the condition for the unbiasedness of decomposed Q-functions.<n>This leads to a general scheme which we call action decomposed reinforcement learning that uses the projected Q-functions to approximate the Q-function in standard model-free reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2025-04-30T05:26:51Z)
• ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning [46.67828766038463]
  We propose a framework, Adaptive Conservative Level in Q-Learning (ACL-QL), which limits the Q-values in a mild range.<n>ACL-QL enables adaptive control on the conservative level over each state-action pair, i.e., lifting the Q-values more for good transitions and less for bad transitions.<n>Motivated by the theoretical analysis, we propose a novel algorithm, ACL-QL, which uses two learnable adaptive weight functions to control the conservative level over each transition.
  arXiv  Detail & Related papers  (2024-12-22T04:18:02Z)
• Mitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functions [18.643104368680593]
  In reinforcement learning, off-policy actor-critic approaches like DDPG and TD3 are based on the deterministic policy gradient. We introduce a new actor architecture that combines two simple insights: (i) use multiple actors and evaluate the Q-value maximizing action, and (ii) learn surrogates to the Q-function that are simpler to optimize with gradient-based methods.
  arXiv  Detail & Related papers  (2024-10-15T17:58:03Z)
• Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization [49.362750475706235]
  Reinforcement Learning (RL) plays a crucial role in aligning large language models with human preferences and improving their ability to perform complex tasks.<n>We introduce Direct Q-function Optimization (DQO), which formulates the response generation process as a Markov Decision Process (MDP) and utilizes the soft actor-critic (SAC) framework to optimize a Q-function directly parameterized by the language model.<n> Experimental results on two math problem-solving datasets, GSM8K and MATH, demonstrate that DQO outperforms previous methods, establishing it as a promising offline reinforcement learning approach for aligning language models.
  arXiv  Detail & Related papers  (2024-10-11T23:29:20Z)
• Q-WSL: Optimizing Goal-Conditioned RL with Weighted Supervised Learning via Dynamic Programming [22.359171999254706]
  A novel class of advanced algorithms, termed GoalConditioned Weighted Supervised Learning (GCWSL), has recently emerged to tackle the challenges posed by sparse rewards in goal-conditioned reinforcement learning (RL)<n>GCWSL consistently delivers strong performance across a diverse set of goal-reaching tasks due to its simplicity, effectiveness, and stability.<n>However, GCWSL methods lack a crucial capability known as trajectory stitching, which is essential for learning optimal policies when faced with unseen skills during testing.<n>In this paper, we propose Q-learning Weighted Supervised Learning (Q-WSL), a novel framework designed to overcome the limitations of GC
  arXiv  Detail & Related papers  (2024-10-09T08:00:12Z)
• Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks [0.24578723416255746]
  In robotics, contemporary strategies are learning-based, characterized by a complex black-box nature and a lack of interpretability. We propose integrating a collision-free trajectory planner based on deep reinforcement learning (DRL) with a novel auto-tuning low-level control strategy.
  arXiv  Detail & Related papers  (2024-02-04T15:54:03Z)
• Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding [55.107555305760954]
  We propose a conceptually simple yet effective method that attributes forgetting to layer-wise parameter overwriting and the resulting decision boundary distortion. Our method achieves competitive accuracy performance, even with absolute superiority of zero exemplar buffer and 1.02x the base model.
  arXiv  Detail & Related papers  (2024-01-17T09:01:29Z)
• Actively Learning Reinforcement Learning: A Stochastic Optimal Control Approach [3.453622106101339]
  We propose a framework towards achieving two intertwined objectives: (i) equipping reinforcement learning with active exploration and deliberate information gathering, and (ii) overcoming the computational intractability of optimal control law. We approach both objectives by using reinforcement learning to compute the optimal control law. Unlike fixed exploration and exploitation balance, caution and probing are employed automatically by the controller in real-time, even after the learning process is terminated.
  arXiv  Detail & Related papers  (2023-09-18T18:05:35Z)
• Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior [51.60683890503293]
  We propose a theoretical framework for studying behavior cloning of complex expert demonstrations using generative modeling. We show that pure supervised cloning can generate trajectories matching the per-time step distribution of arbitrary expert trajectories.
  arXiv  Detail & Related papers  (2023-07-27T04:27:26Z)
• Solving Continuous Control via Q-learning [54.05120662838286]
  We show that a simple modification of deep Q-learning largely alleviates issues with actor-critic methods. By combining bang-bang action discretization with value decomposition, framing single-agent control as cooperative multi-agent reinforcement learning (MARL), this simple critic-only approach matches performance of state-of-the-art continuous actor-critic methods.
  arXiv  Detail & Related papers  (2022-10-22T22:55:50Z)
• Few-shot Quality-Diversity Optimization [50.337225556491774]
  Quality-Diversity (QD) optimization has been shown to be effective tools in dealing with deceptive minima and sparse rewards in Reinforcement Learning. We show that, given examples from a task distribution, information about the paths taken by optimization in parameter space can be leveraged to build a prior population, which when used to initialize QD methods in unseen environments, allows for few-shot adaptation. Experiments carried in both sparse and dense reward settings using robotic manipulation and navigation benchmarks show that it considerably reduces the number of generations that are required for QD optimization in these environments.
  arXiv  Detail & Related papers  (2021-09-14T17:12:20Z)
• Safe-Critical Modular Deep Reinforcement Learning with Temporal Logic through Gaussian Processes and Control Barrier Functions [3.5897534810405403]
  Reinforcement learning (RL) is a promising approach and has limited success towards real-world applications. In this paper, we propose a learning-based control framework consisting of several aspects. We show such an ECBF-based modular deep RL algorithm achieves near-perfect success rates and guard safety with a high probability.
  arXiv  Detail & Related papers  (2021-09-07T00:51:12Z)
• Online Reinforcement Learning Control by Direct Heuristic Dynamic Programming: from Time-Driven to Event-Driven [80.94390916562179]
  Time-driven learning refers to the machine learning method that updates parameters in a prediction model continuously as new data arrives. It is desirable to prevent the time-driven dHDP from updating due to insignificant system event such as noise. We show how the event-driven dHDP algorithm works in comparison to the original time-driven dHDP.
  arXiv  Detail & Related papers  (2020-06-16T05:51:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.