Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data

• URL: http://arxiv.org/abs/2403.17091v1
• Date: Mon, 25 Mar 2024 18:28:45 GMT
• Title: Offline Reinforcement Learning: Role of State Aggregation and Trajectory Data
• Authors: Zeyu Jia, Alexander Rakhlin, Ayush Sekhari, Chen-Yu Wei,
• Abstract summary: We revisit the problem of offline reinforcement learning with value function realizability but without Bellman. Previous work by Xie and Jiang (2021) and Foster et al. (2022) left open the question whether a concentrability coefficient along with trajectory-based offline data unifies a bounded sample complexity.
• Score: 64.93285984013833
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We revisit the problem of offline reinforcement learning with value function realizability but without Bellman completeness. Previous work by Xie and Jiang (2021) and Foster et al. (2022) left open the question whether a bounded concentrability coefficient along with trajectory-based offline data admits a polynomial sample complexity. In this work, we provide a negative answer to this question for the task of offline policy evaluation. In addition to addressing this question, we provide a rather complete picture for offline policy evaluation with only value function realizability. Our primary findings are threefold: 1) The sample complexity of offline policy evaluation is governed by the concentrability coefficient in an aggregated Markov Transition Model jointly determined by the function class and the offline data distribution, rather than that in the original MDP. This unifies and generalizes the ideas of Xie and Jiang (2021) and Foster et al. (2022), 2) The concentrability coefficient in the aggregated Markov Transition Model may grow exponentially with the horizon length, even when the concentrability coefficient in the original MDP is small and the offline data is admissible (i.e., the data distribution equals the occupancy measure of some policy), 3) Under value function realizability, there is a generic reduction that can convert any hard instance with admissible data to a hard instance with trajectory data, implying that trajectory data offers no extra benefits over admissible data. These three pieces jointly resolve the open problem, though each of them could be of independent interest.

Related papers

• Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data [17.991833729722288]
  We propose a novel policy learning algorithm, PESsimistic CAusal Learning (PESCAL) Our key observation is that, by incorporating auxiliary variables that mediate the effect of actions on system dynamics, it is sufficient to learn a lower bound of the mediator distribution function, instead of the Q-function. We provide theoretical guarantees for the algorithms we propose, and demonstrate their efficacy through simulations, as well as real-world experiments utilizing offline datasets from a leading ride-hailing platform.
  arXiv  Detail & Related papers  (2024-03-18T14:51:19Z)
• Provable Offline Preference-Based Reinforcement Learning [95.00042541409901]
  We investigate the problem of offline Preference-based Reinforcement Learning (PbRL) with human feedback. We consider the general reward setting where the reward can be defined over the whole trajectory. We introduce a new single-policy concentrability coefficient, which can be upper bounded by the per-trajectory concentrability.
  arXiv  Detail & Related papers  (2023-05-24T07:11:26Z)
• Offline Reinforcement Learning with Additional Covering Distributions [0.0]
  We study learning optimal policies from a logged dataset, i.e., offline RL, with function approximation. We show that sample-efficient offline RL for general MDPs is possible with only a partial coverage dataset and weak realizable function classes.
  arXiv  Detail & Related papers  (2023-05-22T03:31:03Z)
• Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets [101.5329678997916]
  We study episodic two-player zero-sum Markov games (MGs) in the offline setting. The goal is to find an approximate Nash equilibrium (NE) policy pair based on a dataset collected a priori.
  arXiv  Detail & Related papers  (2022-02-15T15:39:30Z)
• Offline Reinforcement Learning with Realizability and Single-policy Concentrability [40.15976281104956]
  Sample-efficiency guarantees for offline reinforcement learning often rely on strong assumptions on both the function classes and the data coverage. We analyze a simple algorithm based on primal-dual MDPs, where the dual variables are modeled using offline function against offline data.
  arXiv  Detail & Related papers  (2022-02-09T18:51:24Z)
• Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation [74.3002974673248]
  We consider the offline reinforcement learning problem, where the aim is to learn a decision making policy from logged data. offline RL is becoming increasingly relevant in practice, because online data collection is well suited to safety-critical domains. Our results show that sample-efficient offline reinforcement learning requires either restrictive coverage conditions or representation conditions that go beyond complexity learning.
  arXiv  Detail & Related papers  (2021-11-21T23:22:37Z)
• What are the Statistical Limits of Offline RL with Linear Function Approximation? [70.33301077240763]
  offline reinforcement learning seeks to utilize offline (observational) data to guide the learning of sequential decision making strategies. This work focuses on the basic question of what are necessary representational and distributional conditions that permit provable sample-efficient offline reinforcement learning.
  arXiv  Detail & Related papers  (2020-10-22T17:32:13Z)
• 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.