Provable Representation Learning for Imitation with Contrastive Fourier Features

• URL: http://arxiv.org/abs/2105.12272v1
• Date: Wed, 26 May 2021 00:31:30 GMT
• Title: Provable Representation Learning for Imitation with Contrastive Fourier Features
• Authors: Ofir Nachum, Mengjiao Yang
• Abstract summary: We consider using offline experience datasets to learn low-dimensional state representations. A central challenge is that the unknown target policy itself may not exhibit low-dimensional behavior. We derive a representation learning objective which provides an upper bound on the performance difference between the target policy and a lowdimensional policy trained with max-likelihood.
• Score: 27.74988221252854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In imitation learning, it is common to learn a behavior policy to match an unknown target policy via max-likelihood training on a collected set of target demonstrations. In this work, we consider using offline experience datasets - potentially far from the target distribution - to learn low-dimensional state representations that provably accelerate the sample-efficiency of downstream imitation learning. A central challenge in this setting is that the unknown target policy itself may not exhibit low-dimensional behavior, and so there is a potential for the representation learning objective to alias states in which the target policy acts differently. Circumventing this challenge, we derive a representation learning objective which provides an upper bound on the performance difference between the target policy and a lowdimensional policy trained with max-likelihood, and this bound is tight regardless of whether the target policy itself exhibits low-dimensional structure. Moving to the practicality of our method, we show that our objective can be implemented as contrastive learning, in which the transition dynamics are approximated by either an implicit energy-based model or, in some special cases, an implicit linear model with representations given by random Fourier features. Experiments on both tabular environments and high-dimensional Atari games provide quantitative evidence for the practical benefits of our proposed objective.

Related papers

• Zero-Shot Offline Imitation Learning via Optimal Transport [21.548195072895517]
  Zero-shot imitation learning algorithms reproduce unseen behavior from as little as a single demonstration at test time. Existing practical approaches view the expert demonstration as a sequence of goals, enabling imitation with a high-level goal selector, and a low-level goal-conditioned policy. We introduce a novel method that mitigates this issue by directly optimizing the occupancy matching objective that is intrinsic to imitation learning.
  arXiv  Detail & Related papers  (2024-10-11T12:10:51Z)
• Zero-Shot Object-Centric Representation Learning [72.43369950684057]
  We study current object-centric methods through the lens of zero-shot generalization. We introduce a benchmark comprising eight different synthetic and real-world datasets. We find that training on diverse real-world images improves transferability to unseen scenarios.
  arXiv  Detail & Related papers  (2024-08-17T10:37:07Z)
• Goal-conditioned Offline Planning from Curious Exploration [28.953718733443143]
  We consider the challenge of extracting goal-conditioned behavior from the products of unsupervised exploration techniques. We find that conventional goal-conditioned reinforcement learning approaches for extracting a value function and policy fall short in this difficult offline setting. In order to mitigate their occurrence, we propose to combine model-based planning over learned value landscapes with a graph-based value aggregation scheme.
  arXiv  Detail & Related papers  (2023-11-28T17:48:18Z)
• Reparameterized Policy Learning for Multimodal Trajectory Optimization [61.13228961771765]
  We investigate the challenge of parametrizing policies for reinforcement learning in high-dimensional continuous action spaces. We propose a principled framework that models the continuous RL policy as a generative model of optimal trajectories. We present a practical model-based RL method, which leverages the multimodal policy parameterization and learned world model.
  arXiv  Detail & Related papers  (2023-07-20T09:05:46Z)
• TarGF: Learning Target Gradient Field for Object Rearrangement [8.49306925839127]
  We focus on a more practical setting in object rearrangement, i.e., rearranging objects from shuffled layouts to a normative target distribution. It is hard to describe the target distribution (goal specification) for reward engineering or collect expert trajectories as demonstrations. We employ the score-matching objective to train a Target Gradient Field (TarGF), indicating a direction on each object to increase the likelihood of the target distribution.
  arXiv  Detail & Related papers  (2022-09-02T07:20:34Z)
• Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space [76.46113138484947]
  General-purpose robots require diverse repertoires of behaviors to complete challenging tasks in real-world unstructured environments. To address this issue, goal-conditioned reinforcement learning aims to acquire policies that can reach goals for a wide range of tasks on command. We propose Planning to Practice, a method that makes it practical to train goal-conditioned policies for long-horizon tasks.
  arXiv  Detail & Related papers  (2022-05-17T06:58:17Z)
• Adversarial Intrinsic Motivation for Reinforcement Learning [60.322878138199364]
  We investigate whether the Wasserstein-1 distance between a policy's state visitation distribution and a target distribution can be utilized effectively for reinforcement learning tasks. Our approach, termed Adversarial Intrinsic Motivation (AIM), estimates this Wasserstein-1 distance through its dual objective and uses it to compute a supplemental reward function.
  arXiv  Detail & Related papers  (2021-05-27T17:51:34Z)
• Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning [9.014110264448371]
  We propose a novel unsupervised learning approach named goal-conditioned policy with intrinsic motivation (GPIM) GPIM jointly learns both an abstract-level policy and a goal-conditioned policy. Experiments on various robotic tasks demonstrate the effectiveness and efficiency of our proposed GPIM method.
  arXiv  Detail & Related papers  (2021-04-11T16:26:10Z)
• Goal-Aware Prediction: Learning to Model What Matters [105.43098326577434]
  One of the fundamental challenges in using a learned forward dynamics model is the mismatch between the objective of the learned model and that of the downstream planner or policy. We propose to direct prediction towards task relevant information, enabling the model to be aware of the current task and encouraging it to only model relevant quantities of the state space. We find that our method more effectively models the relevant parts of the scene conditioned on the goal, and as a result outperforms standard task-agnostic dynamics models and model-free reinforcement learning.
  arXiv  Detail & Related papers  (2020-07-14T16:42:59Z)
• Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning [75.56839075060819]
  Traditional robotic approaches rely on an accurate model of the environment, a detailed description of how to perform the task, and a robust perception system to keep track of the current state. reinforcement learning approaches can operate directly from raw sensory inputs with only a reward signal to describe the task, but are extremely sample-inefficient and brittle. In this work, we combine the strengths of model-based methods with the flexibility of learning-based methods to obtain a general method that is able to overcome inaccuracies in the robotics perception/actuation pipeline.
  arXiv  Detail & Related papers  (2020-05-21T19:47:05Z)
• Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts [24.489002406693128]
  We introduce a novel mixture-of-experts formulation for learning state-dependent beliefs over source task dynamics. We show how this model can be incorporated into standard policy reuse frameworks.
  arXiv  Detail & Related papers  (2020-02-29T07:58:36Z)

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.