Q-function Decomposition with Intervention Semantics with Factored Action Spaces

• URL: http://arxiv.org/abs/2504.21326v1
• Date: Wed, 30 Apr 2025 05:26:51 GMT
• Title: Q-function Decomposition with Intervention Semantics with Factored Action Spaces
• Authors: Junkyu Lee, Tian Gao, Elliot Nelson, Miao Liu, Debarun Bhattacharjya, Songtao Lu,
• Abstract summary: 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.
• Score: 51.01244229483353
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many practical reinforcement learning environments have a discrete factored action space that induces a large combinatorial set of actions, thereby posing significant challenges. Existing approaches leverage the regular structure of the action space and resort to a linear decomposition of Q-functions, which avoids enumerating all combinations of factored actions. In this paper, 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 using causal effect estimation from the no unobserved confounder setting in causal statistics. 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. The proposed approach is shown to improve sample complexity in a model-based reinforcement learning setting. We demonstrate improvements in sample efficiency compared to state-of-the-art baselines in online continuous control environments and a real-world offline sepsis treatment environment.

Related papers

• Kernel-based estimators for functional causal effects [1.6749379740049928]
  We propose causal effect estimators based on empirical Fr'echet means and operator-valued kernels, tailored to functional data spaces.<n>These methods address the challenges of high-dimensionality, sequential ordering, and model complexity while preserving robustness to treatment misspecification.
  arXiv  Detail & Related papers  (2025-03-06T22:48:55Z)
• Stable Inverse Reinforcement Learning: Policies from Control Lyapunov Landscapes [4.229902091180109]
  We propose a novel, stability-certified IRL approach to learning control Lyapunov functions from demonstrations data. By exploiting closed-form expressions for associated control policies, we are able to efficiently search the space of CLFs. We present a theoretical analysis of the optimality properties provided by the CLF and evaluate our approach using both simulated and real-world data.
  arXiv  Detail & Related papers  (2024-05-14T16:40:45Z)
• Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement [67.1393112206885]
  Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. We introduce Entropy-Regularized Token-level Policy Optimization (ETPO), an entropy-augmented RL method tailored for optimizing LLMs at the token level. We assess the effectiveness of ETPO within a simulated environment that models data science code generation as a series of multi-step interactive tasks.
  arXiv  Detail & Related papers  (2024-02-09T07:45:26Z)
• Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback [31.115084475673793]
  The ensemble method is a promising way to mitigate the overestimation issue in Q-learning. It is known that the estimation bias hinges heavily on the ensemble size. We devise an ensemble method with two key steps: (a) approximation error characterization which serves as the feedback for flexibly controlling the ensemble size, and (b) ensemble size adaptation tailored towards minimizing the estimation bias.
  arXiv  Detail & Related papers  (2023-06-20T22:06:14Z)
• Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient [65.08966446962845]
  offline reinforcement learning, which aims at optimizing decision-making strategies with historical data, has been extensively applied in real-life applications. We take a step by considering offline reinforcement learning with differentiable function class approximation (DFA) Most importantly, we show offline differentiable function approximation is provably efficient by analyzing the pessimistic fitted Q-learning algorithm.
  arXiv  Detail & Related papers  (2022-10-03T07:59:42Z)
• Stabilizing Q-learning with Linear Architectures for Provably Efficient Learning [53.17258888552998]
  This work proposes an exploration variant of the basic $Q$-learning protocol with linear function approximation. We show that the performance of the algorithm degrades very gracefully under a novel and more permissive notion of approximation error.
  arXiv  Detail & Related papers  (2022-06-01T23:26:51Z)
• Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling [28.371541697552928]
  We present the first result for non-linear function approximation which holds for general action spaces under a linear embeddability condition. We show worst case sample complexity guarantees that scale with a rank parameter of the RL problem.
  arXiv  Detail & Related papers  (2022-03-15T20:50:26Z)
• Ideal formulations for constrained convex optimization problems with indicator variables [2.578242050187029]
  We study the convexification of a class of convex optimization problems with indicator variables and constraints on the indicators. Unlike most of the previous work on convexification of sparse regression problems, we simultaneously consider the nonlinear non-separable objective, indicator variables, and constraints. We derive ideal convexifications for problems with hierarchy, multi-collinearity, and sparsity constraints.
  arXiv  Detail & Related papers  (2020-06-30T21:07:10Z)
• Invariant Causal Prediction for Block MDPs [106.63346115341862]
  Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challenges. We propose a method of invariant prediction to learn model-irrelevance state abstractions (MISA) that generalize to novel observations in the multi-environment setting.
  arXiv  Detail & Related papers  (2020-03-12T21:03:01Z)
• Discrete Action On-Policy Learning with Action-Value Critic [72.20609919995086]
  Reinforcement learning (RL) in discrete action space is ubiquitous in real-world applications, but its complexity grows exponentially with the action-space dimension. We construct a critic to estimate action-value functions, apply it on correlated actions, and combine these critic estimated action values to control the variance of gradient estimation. These efforts result in a new discrete action on-policy RL algorithm that empirically outperforms related on-policy algorithms relying on variance control techniques.
  arXiv  Detail & Related papers  (2020-02-10T04:23:09Z)

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.