Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments

• URL: http://arxiv.org/abs/2310.08841v1
• Date: Fri, 13 Oct 2023 03:39:15 GMT
• Title: Leveraging Optimal Transport for Enhanced Offline Reinforcement Learning in Surgical Robotic Environments
• Authors: Maryam Zare, Parham M. Kebria, Abbas Khosravi
• Abstract summary: We introduce an innovative algorithm designed to assign rewards to offline trajectories, using a small number of high-quality expert demonstrations. This approach circumvents the need for handcrafted rewards, unlocking the potential to harness vast datasets for policy learning.
• Score: 4.2569494803130565
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most Reinforcement Learning (RL) methods are traditionally studied in an active learning setting, where agents directly interact with their environments, observe action outcomes, and learn through trial and error. However, allowing partially trained agents to interact with real physical systems poses significant challenges, including high costs, safety risks, and the need for constant supervision. Offline RL addresses these cost and safety concerns by leveraging existing datasets and reducing the need for resource-intensive real-time interactions. Nevertheless, a substantial challenge lies in the demand for these datasets to be meticulously annotated with rewards. In this paper, we introduce Optimal Transport Reward (OTR) labelling, an innovative algorithm designed to assign rewards to offline trajectories, using a small number of high-quality expert demonstrations. The core principle of OTR involves employing Optimal Transport (OT) to calculate an optimal alignment between an unlabeled trajectory from the dataset and an expert demonstration. This alignment yields a similarity measure that is effectively interpreted as a reward signal. An offline RL algorithm can then utilize these reward signals to learn a policy. This approach circumvents the need for handcrafted rewards, unlocking the potential to harness vast datasets for policy learning. Leveraging the SurRoL simulation platform tailored for surgical robot learning, we generate datasets and employ them to train policies using the OTR algorithm. By demonstrating the efficacy of OTR in a different domain, we emphasize its versatility and its potential to expedite RL deployment across a wide range of fields.

Related papers

• Real-World Offline Reinforcement Learning from Vision Language Model Feedback [19.494335952082466]
  offline reinforcement learning can enable policy learning from pre-collected, sub-optimal datasets without online interactions. Most existing offline RL works assume the dataset is already labeled with the task rewards. We propose a novel system that automatically generates reward labels for offline datasets.
  arXiv  Detail & Related papers  (2024-11-08T02:12:34Z)
• Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning [67.95280175998792]
  A novel adversarial imitation learning (GAIL)-powered policy learning approach is proposed for optimizing beamforming, spectrum allocation, and remote user equipment (RUE) association ins. We employ inverse RL (IRL) to automatically learn reward functions without manual tuning. We show that the proposed MA-AL method outperforms traditional RL approaches, achieving a $14.6%$ improvement in convergence and reward value.
  arXiv  Detail & Related papers  (2024-09-27T13:05:02Z)
• OffRIPP: Offline RL-based Informative Path Planning [12.705099730591671]
  IPP is a crucial task in robotics, where agents must design paths to gather valuable information about a target environment. We propose an offline RL-based IPP framework that optimized information gain without requiring real-time interaction during training. We validate the framework through extensive simulations and real-world experiments.
  arXiv  Detail & Related papers  (2024-09-25T11:30:59Z)
• D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning [99.33607114541861]
  We propose a new benchmark for offline RL that focuses on realistic simulations of robotic manipulation and locomotion environments. Our proposed benchmark covers state-based and image-based domains, and supports both offline RL and online fine-tuning evaluation.
  arXiv  Detail & Related papers  (2024-08-15T22:27:00Z)
• Advancing RAN Slicing with Offline Reinforcement Learning [15.259182716723496]
  This paper introduces offlineReinforcement Learning to solve the RAN slicing problem. We show how offline RL can effectively learn near-optimal policies from sub-optimal datasets. We also present empirical evidence of the efficacy of offline RL in adapting to various service-level requirements.
  arXiv  Detail & Related papers  (2023-12-16T22:09:50Z)
• Optimal Transport for Offline Imitation Learning [31.218468923400373]
  offline reinforcement learning (RL) is a promising framework for learning good decision-making policies without the need to interact with the real environment. We introduce Optimal Transport Reward labeling (OTR), an algorithm that assigns rewards to offline trajectories. We show that OTR with a single demonstration can consistently match the performance of offline RL with ground-truth rewards.
  arXiv  Detail & Related papers  (2023-03-24T12:45:42Z)
• Benchmarks and Algorithms for Offline Preference-Based Reward Learning [41.676208473752425]
  We propose an approach that uses an offline dataset to craft preference queries via pool-based active learning. Our proposed approach does not require actual physical rollouts or an accurate simulator for either the reward learning or policy optimization steps.
  arXiv  Detail & Related papers  (2023-01-03T23:52:16Z)
• Offline Meta-Reinforcement Learning with Online Self-Supervision [66.42016534065276]
  We propose a hybrid offline meta-RL algorithm, which uses offline data with rewards to meta-train an adaptive policy. Our method uses the offline data to learn the distribution of reward functions, which is then sampled to self-supervise reward labels for the additional online data. We find that using additional data and self-generated rewards significantly improves an agent's ability to generalize.
  arXiv  Detail & Related papers  (2021-07-08T17:01:32Z)
• Learning Dexterous Manipulation from Suboptimal Experts [69.8017067648129]
  Relative Entropy Q-Learning (REQ) is a simple policy algorithm that combines ideas from successful offline and conventional RL algorithms. We show how REQ is also effective for general off-policy RL, offline RL, and RL from demonstrations.
  arXiv  Detail & Related papers  (2020-10-16T18:48:49Z)
• Critic Regularized Regression [70.8487887738354]
  We propose a novel offline RL algorithm to learn policies from data using a form of critic-regularized regression (CRR) We find that CRR performs surprisingly well and scales to tasks with high-dimensional state and action spaces.
  arXiv  Detail & Related papers  (2020-06-26T17:50:26Z)
• AWAC: Accelerating Online Reinforcement Learning with Offline Datasets [84.94748183816547]
  We show that our method, advantage weighted actor critic (AWAC), enables rapid learning of skills with a combination of prior demonstration data and online experience. Our results show that incorporating prior data can reduce the time required to learn a range of robotic skills to practical time-scales.
  arXiv  Detail & Related papers  (2020-06-16T17:54:41Z)

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.