Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off

• URL: http://arxiv.org/abs/2212.08949v3
• Date: Tue, 16 Jan 2024 06:59:29 GMT
• Title: Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off
• Authors: Zichen Zhang, Johannes Kirschner, Junxi Zhang, Francesco Zanini, Alex Ayoub, Masood Dehghan, Dale Schuurmans
• Abstract summary: We show that managing the temporal resolution can improve policy evaluation efficiency in LQR systems with finite data. These findings show that managing the temporal resolution can provably improve policy evaluation efficiency in LQR systems with finite data.
• Score: 39.061605300172175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A default assumption in reinforcement learning (RL) and optimal control is that observations arrive at discrete time points on a fixed clock cycle. Yet, many applications involve continuous-time systems where the time discretization, in principle, can be managed. The impact of time discretization on RL methods has not been fully characterized in existing theory, but a more detailed analysis of its effect could reveal opportunities for improving data-efficiency. We address this gap by analyzing Monte-Carlo policy evaluation for LQR systems and uncover a fundamental trade-off between approximation and statistical error in value estimation. Importantly, these two errors behave differently to time discretization, leading to an optimal choice of temporal resolution for a given data budget. These findings show that managing the temporal resolution can provably improve policy evaluation efficiency in LQR systems with finite data. Empirically, we demonstrate the trade-off in numerical simulations of LQR instances and standard RL benchmarks for non-linear continuous control.

Related papers

• Time-Constrained Robust MDPs [28.641743425443]
  We introduce a new time-constrained robust MDP (TC-RMDP) formulation that considers multifactorial, correlated, and time-dependent disturbances. This study revisits the prevailing assumptions in robust RL and opens new avenues for developing more practical and realistic RL applications.
  arXiv  Detail & Related papers  (2024-06-12T16:45:09Z)
• When to Sense and Control? A Time-adaptive Approach for Continuous-Time RL [37.58940726230092]
  Reinforcement learning (RL) excels in optimizing policies for discrete-time Markov decision processes (MDP) We formalize an RL framework, Time-adaptive Control & Sensing (TaCoS), that tackles this challenge. We demonstrate that state-of-the-art RL algorithms trained on TaCoS drastically reduce the interaction amount over their discrete-time counterpart.
  arXiv  Detail & Related papers  (2024-06-03T09:57:18Z)
• Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL [86.0987896274354]
  We first identify a fundamental pattern, self-excitation, as the primary cause of Q-value estimation divergence in offline RL. We then propose a novel Self-Excite Eigenvalue Measure (SEEM) metric to measure the evolving property of Q-network at training. For the first time, our theory can reliably decide whether the training will diverge at an early stage.
  arXiv  Detail & Related papers  (2023-10-06T17:57:44Z)
• Offline Policy Optimization in RL with Variance Regularizaton [142.87345258222942]
  We propose variance regularization for offline RL algorithms, using stationary distribution corrections. We show that by using Fenchel duality, we can avoid double sampling issues for computing the gradient of the variance regularizer. The proposed algorithm for offline variance regularization (OVAR) can be used to augment any existing offline policy optimization algorithms.
  arXiv  Detail & Related papers  (2022-12-29T18:25:01Z)
• Optimal Conservative Offline RL with General Function Approximation via Augmented Lagrangian [18.2080757218886]
  offline reinforcement learning (RL) refers to decision-making from a previously-collected dataset of interactions. We present the first set of offline RL algorithms that are statistically optimal and practical under general function approximation and single-policy concentrability.
  arXiv  Detail & Related papers  (2022-11-01T19:28:48Z)
• Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations [84.42837346400151]
  Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare. Existing causal inference approaches consider regular, discrete-time intervals between observations and treatment decisions. We propose a controllable simulation environment based on a model of tumor growth for a range of scenarios.
  arXiv  Detail & Related papers  (2022-06-16T17:15:15Z)
• Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes [70.80716221080118]
  The paper takes a generative perspective on policy evaluation via temporal-difference (TD) learning. The OS-GPTD approach is developed to estimate the value function for a given policy by observing a sequence of state-reward pairs. To alleviate the limited expressiveness associated with a single fixed kernel, a weighted ensemble (E) of GP priors is employed to yield an alternative scheme.
  arXiv  Detail & Related papers  (2021-12-01T23:15:09Z)
• Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble [16.92791301062903]
  We propose an uncertainty-based offline RL method that takes into account the confidence of the Q-value prediction and does not require any estimation or sampling of the data distribution. Surprisingly, we find that it is possible to substantially outperform existing offline RL methods on various tasks by simply increasing the number of Q-networks along with the clipped Q-learning.
  arXiv  Detail & Related papers  (2021-10-04T16:40:13Z)
• GenDICE: Generalized Offline Estimation of Stationary Values [108.17309783125398]
  We show that effective estimation can still be achieved in important applications. Our approach is based on estimating a ratio that corrects for the discrepancy between the stationary and empirical distributions. The resulting algorithm, GenDICE, is straightforward and effective.
  arXiv  Detail & Related papers  (2020-02-21T00:27:52Z)

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.