PI-QT-Opt: Predictive Information Improves Multi-Task Robotic Reinforcement Learning at Scale

• URL: http://arxiv.org/abs/2210.08217v1
• Date: Sat, 15 Oct 2022 07:30:31 GMT
• Title: PI-QT-Opt: Predictive Information Improves Multi-Task Robotic Reinforcement Learning at Scale
• Authors: Kuang-Huei Lee, Ted Xiao, Adrian Li, Paul Wohlhart, Ian Fischer, Yao Lu
• Abstract summary: Predictive Information QT-Opt learns representations of the predictive information to solve up to 297 vision-based robot manipulation tasks in simulation and the real world. We demonstrate that modeling the predictive information significantly improves success rates on the training tasks and leads to better zero-shot transfer to unseen novel tasks.
• Score: 14.444439310266873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The predictive information, the mutual information between the past and future, has been shown to be a useful representation learning auxiliary loss for training reinforcement learning agents, as the ability to model what will happen next is critical to success on many control tasks. While existing studies are largely restricted to training specialist agents on single-task settings in simulation, in this work, we study modeling the predictive information for robotic agents and its importance for general-purpose agents that are trained to master a large repertoire of diverse skills from large amounts of data. Specifically, we introduce Predictive Information QT-Opt (PI-QT-Opt), a QT-Opt agent augmented with an auxiliary loss that learns representations of the predictive information to solve up to 297 vision-based robot manipulation tasks in simulation and the real world with a single set of parameters. We demonstrate that modeling the predictive information significantly improves success rates on the training tasks and leads to better zero-shot transfer to unseen novel tasks. Finally, we evaluate PI-QT-Opt on real robots, achieving substantial and consistent improvement over QT-Opt in multiple experimental settings of varying environments, skills, and multi-task configurations.

Related papers

• Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning [58.3994826169858]
  We introduce RoboFuME, a reset-free fine-tuning system for robotic reinforcement learning. Our insights are to utilize offline reinforcement learning techniques to ensure efficient online fine-tuning of a pre-trained policy. Our method can incorporate data from an existing robot dataset and improve on a target task within as little as 3 hours of autonomous real-world experience.
  arXiv  Detail & Related papers  (2023-10-23T17:50:08Z)
• Analysis of the Reasoning with Redundant Information Provided Ability of Large Language Models [0.0]
  Large Language Models (LLMs) have demonstrated impressive capabilities across a range of natural language processing tasks. To address this gap, a new form of Question-Answering (QA) task, termed Reasoning with Redundant Information Provided (RRIP), is introduced. This study evaluates two popular LLMs, LlaMA2-13B-chat and generative pre-trained transformer 3.5 (GPT-3.5), contrasting their performance on traditional QA tasks against RRIP tasks.
  arXiv  Detail & Related papers  (2023-10-06T06:20:06Z)
• Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials [97.95400776235736]
  We present a framework based on offline RL that attempts to effectively learn new tasks. It combines pre-training on existing robotic datasets with rapid fine-tuning on a new task, with as few as 10 demonstrations. To our knowledge, PTR is the first RL method that succeeds at learning new tasks in a new domain on a real WidowX robot.
  arXiv  Detail & Related papers  (2022-10-11T06:30:53Z)
• Hierarchical Few-Shot Imitation with Skill Transition Models [66.81252581083199]
  Few-shot Imitation with Skill Transition Models (FIST) is an algorithm that extracts skills from offline data and utilizes them to generalize to unseen tasks. We show that FIST is capable of generalizing to new tasks and substantially outperforms prior baselines in navigation experiments.
  arXiv  Detail & Related papers  (2021-07-19T15:56:01Z)
• Pre-Trained Models: Past, Present and Future [126.21572378910746]
  Large-scale pre-trained models (PTMs) have recently achieved great success and become a milestone in the field of artificial intelligence (AI) By storing knowledge into huge parameters and fine-tuning on specific tasks, the rich knowledge implicitly encoded in huge parameters can benefit a variety of downstream tasks. It is now the consensus of the AI community to adopt PTMs as backbone for downstream tasks rather than learning models from scratch.
  arXiv  Detail & Related papers  (2021-06-14T02:40:32Z)
• Parrot: Data-Driven Behavioral Priors for Reinforcement Learning [79.32403825036792]
  We propose a method for pre-training behavioral priors that can capture complex input-output relationships observed in successful trials. We show how this learned prior can be used for rapidly learning new tasks without impeding the RL agent's ability to try out novel behaviors.
  arXiv  Detail & Related papers  (2020-11-19T18:47:40Z)
• Scalable Multi-Task Imitation Learning with Autonomous Improvement [159.9406205002599]
  We build an imitation learning system that can continuously improve through autonomous data collection. We leverage the robot's own trials as demonstrations for tasks other than the one that the robot actually attempted. In contrast to prior imitation learning approaches, our method can autonomously collect data with sparse supervision for continuous improvement.
  arXiv  Detail & Related papers  (2020-02-25T18:56:42Z)

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.