Provable Representation Learning for Imitation Learning via Bi-level Optimization

• URL: http://arxiv.org/abs/2002.10544v1
• Date: Mon, 24 Feb 2020 21:03:52 GMT
• Title: Provable Representation Learning for Imitation Learning via Bi-level Optimization
• Authors: Sanjeev Arora, Simon S. Du, Sham Kakade, Yuping Luo, and Nikunj Saunshi
• Abstract summary: A common strategy in modern learning systems is to learn a representation that is useful for many tasks. We study this strategy in the imitation learning setting for Markov decision processes (MDPs) where multiple experts' trajectories are available. We instantiate this framework for the imitation learning settings of behavior cloning and observation-alone.
• Score: 60.059520774789654
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A common strategy in modern learning systems is to learn a representation that is useful for many tasks, a.k.a. representation learning. We study this strategy in the imitation learning setting for Markov decision processes (MDPs) where multiple experts' trajectories are available. We formulate representation learning as a bi-level optimization problem where the "outer" optimization tries to learn the joint representation and the "inner" optimization encodes the imitation learning setup and tries to learn task-specific parameters. We instantiate this framework for the imitation learning settings of behavior cloning and observation-alone. Theoretically, we show using our framework that representation learning can provide sample complexity benefits for imitation learning in both settings. We also provide proof-of-concept experiments to verify our theory.

Related papers

• Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method [7.261306002808739]
  We construct a theoretical analysis framework for prompt-based federated learning via feature learning theory. Specifically, we monitor the evolution of signal learning and noise memorization in prompt-based federated learning. We show that performance can be assessed by the ratio of task-relevant to task-irrelevant coefficients.
  arXiv  Detail & Related papers  (2024-09-29T08:31:26Z)
• Self-Supervised Representation Learning with Meta Comprehensive Regularization [11.387994024747842]
  We introduce a module called CompMod with Meta Comprehensive Regularization (MCR), embedded into existing self-supervised frameworks. We update our proposed model through a bi-level optimization mechanism, enabling it to capture comprehensive features. We provide theoretical support for our proposed method from information theory and causal counterfactual perspective.
  arXiv  Detail & Related papers  (2024-03-03T15:53:48Z)
• RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
  We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning. Our proposed method uses reinforcement learning with user intervention signals themselves as rewards. This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
  arXiv  Detail & Related papers  (2023-11-21T21:05:21Z)
• Sample-efficient Adversarial Imitation Learning [45.400080101596956]
  We propose a self-supervised representation-based adversarial imitation learning method to learn state and action representations. We show a 39% relative improvement over existing adversarial imitation learning methods on MuJoCo in a setting limited to 100 expert state-action pairs.
  arXiv  Detail & Related papers  (2023-03-14T12:36:01Z)
• Understanding Self-Predictive Learning for Reinforcement Learning [61.62067048348786]
  We study the learning dynamics of self-predictive learning for reinforcement learning. We propose a novel self-predictive algorithm that learns two representations simultaneously.
  arXiv  Detail & Related papers  (2022-12-06T20:43:37Z)
• Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning [92.18524491615548]
  Contrastive self-supervised learning has been successfully integrated into the practice of (deep) reinforcement learning (RL) We study how RL can be empowered by contrastive learning in a class of Markov decision processes (MDPs) and Markov games (MGs) with low-rank transitions. Under the online setting, we propose novel upper confidence bound (UCB)-type algorithms that incorporate such a contrastive loss with online RL algorithms for MDPs or MGs.
  arXiv  Detail & Related papers  (2022-07-29T17:29:08Z)
• An Empirical Investigation of Representation Learning for Imitation [76.48784376425911]
  Recent work in vision, reinforcement learning, and NLP has shown that auxiliary representation learning objectives can reduce the need for large amounts of expensive, task-specific data. We propose a modular framework for constructing representation learning algorithms, then use our framework to evaluate the utility of representation learning for imitation.
  arXiv  Detail & Related papers  (2022-05-16T11:23:42Z)
• The Sample Complexity of Teaching-by-Reinforcement on Q-Learning [40.37954633873304]
  We study the sample complexity of teaching, termed as "teaching dimension" (TDim) in the literature, for the teaching-by-reinforcement paradigm. In this paper, we focus on a specific family of reinforcement learning algorithms, Q-learning, and characterize the TDim under different teachers with varying control power over the environment. Our TDim results provide the minimum number of samples needed for reinforcement learning, and we discuss their connections to standard PAC-style RL sample complexity and teaching-by-demonstration sample complexity results.
  arXiv  Detail & Related papers  (2020-06-16T17:06:04Z)
• Guided Variational Autoencoder for Disentanglement Learning [79.02010588207416]
  We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to learn a controllable generative model by performing latent representation disentanglement learning. We design an unsupervised strategy and a supervised strategy in Guided-VAE and observe enhanced modeling and controlling capability over the vanilla VAE.
  arXiv  Detail & Related papers  (2020-04-02T20:49:15Z)

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.