STEEL: Singularity-aware Reinforcement Learning

• URL: http://arxiv.org/abs/2301.13152v5
• Date: Wed, 26 Jun 2024 03:39:39 GMT
• Title: STEEL: Singularity-aware Reinforcement Learning
• Authors: Xiaohong Chen, Zhengling Qi, Runzhe Wan,
• Abstract summary: Batch reinforcement learning (RL) aims at leveraging pre-collected data to find an optimal policy. We propose a new batch RL algorithm that allows for singularity for both state and action spaces. By leveraging the idea of pessimism and under some technical conditions, we derive a first finite-sample regret guarantee for our proposed algorithm.
• Score: 14.424199399139804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Batch reinforcement learning (RL) aims at leveraging pre-collected data to find an optimal policy that maximizes the expected total rewards in a dynamic environment. The existing methods require absolutely continuous assumption (e.g., there do not exist non-overlapping regions) on the distribution induced by target policies with respect to the data distribution over either the state or action or both. We propose a new batch RL algorithm that allows for singularity for both state and action spaces (e.g., existence of non-overlapping regions between offline data distribution and the distribution induced by the target policies) in the setting of an infinite-horizon Markov decision process with continuous states and actions. We call our algorithm STEEL: SingulariTy-awarE rEinforcement Learning. Our algorithm is motivated by a new error analysis on off-policy evaluation, where we use maximum mean discrepancy, together with distributionally robust optimization, to characterize the error of off-policy evaluation caused by the possible singularity and to enable model extrapolation. By leveraging the idea of pessimism and under some technical conditions, we derive a first finite-sample regret guarantee for our proposed algorithm under singularity. Compared with existing algorithms,by requiring only minimal data-coverage assumption, STEEL improves the applicability and robustness of batch RL. In addition, a two-step adaptive STEEL, which is nearly tuning-free, is proposed. Extensive simulation studies and one (semi)-real experiment on personalized pricing demonstrate the superior performance of our methods in dealing with possible singularity in batch RL.

Related papers

• Distributional Off-policy Evaluation with Bellman Residual Minimization [12.343981093497332]
  We study distributional off-policy evaluation (OPE) The goal is to learn the distribution of the return for a target policy using offline data generated by a different policy. We propose a new method called Energy Bellman Residual Minimizer (EBRM)
  arXiv  Detail & Related papers  (2024-02-02T20:59:29Z)
• Bi-Level Offline Policy Optimization with Limited Exploration [1.8130068086063336]
  We study offline reinforcement learning (RL) which seeks to learn a good policy based on a fixed, pre-collected dataset. We propose a bi-level structured policy optimization algorithm that models a hierarchical interaction between the policy (upper-level) and the value function (lower-level) We evaluate our model using a blend of synthetic, benchmark, and real-world datasets for offline RL, showing that it performs competitively with state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-10-10T02:45:50Z)
• 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)
• A Strong Baseline for Batch Imitation Learning [25.392006064406967]
  We provide an easy-to-implement, novel algorithm for imitation learning under a strict data paradigm. This paradigm allows our algorithm to be used for environments in which safety or cost are of critical concern.
  arXiv  Detail & Related papers  (2023-02-06T14:03:33Z)
• 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)
• Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm [4.128216503196621]
  We propose an On-policy Model-based Safe Deep RL algorithm in which we learn the transition dynamics of the environment in an online manner. We show that our algorithm is more sample efficient and results in lower cumulative hazard violations as compared to constrained model-free approaches.
  arXiv  Detail & Related papers  (2022-10-14T06:53:02Z)
• 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)
• MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning [108.79676336281211]
  Continuous deployment of new policies for data collection and online learning is either cost ineffective or impractical. We propose a new algorithmic learning framework called Model-based Uncertainty regularized and Sample Efficient Batch Optimization. Our framework discovers novel and high quality samples for each deployment to enable efficient data collection.
  arXiv  Detail & Related papers  (2021-02-23T01:30:55Z)
• Continuous Doubly Constrained Batch Reinforcement Learning [93.23842221189658]
  We propose an algorithm for batch RL, where effective policies are learned using only a fixed offline dataset instead of online interactions with the environment. The limited data in batch RL produces inherent uncertainty in value estimates of states/actions that were insufficiently represented in the training data. We propose to mitigate this issue via two straightforward penalties: a policy-constraint to reduce this divergence and a value-constraint that discourages overly optimistic estimates.
  arXiv  Detail & Related papers  (2021-02-18T08:54:14Z)
• Provably Good Batch Reinforcement Learning Without Great Exploration [51.51462608429621]
  Batch reinforcement learning (RL) is important to apply RL algorithms to many high stakes tasks. Recent algorithms have shown promise but can still be overly optimistic in their expected outcomes. We show that a small modification to Bellman optimality and evaluation back-up to take a more conservative update can have much stronger guarantees.
  arXiv  Detail & Related papers  (2020-07-16T09:25:54Z)

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.