Imitation from Observation With Bootstrapped Contrastive Learning
- URL: http://arxiv.org/abs/2302.06540v1
- Date: Mon, 13 Feb 2023 17:32:17 GMT
- Title: Imitation from Observation With Bootstrapped Contrastive Learning
- Authors: Medric Sonwa, Johanna Hansen, Eugene Belilovsky
- Abstract summary: Imitation from observation (IfO) is a learning paradigm that consists of training autonomous agents in a Markov Decision Process.
We present BootIfOL, an IfO algorithm that aims to learn a reward function that takes an agent trajectory and compares it to an expert.
We evaluate our approach on a variety of control tasks showing that we can train effective policies using a limited number of demonstrative trajectories.
- Score: 12.048166025000976
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Imitation from observation (IfO) is a learning paradigm that consists of
training autonomous agents in a Markov Decision Process (MDP) by observing
expert demonstrations without access to its actions. These demonstrations could
be sequences of environment states or raw visual observations of the
environment. Recent work in IfO has focused on this problem in the case of
observations of low-dimensional environment states, however, access to these
highly-specific observations is unlikely in practice. In this paper, we adopt a
challenging, but more realistic problem formulation, learning control policies
that operate on a learned latent space with access only to visual
demonstrations of an expert completing a task. We present BootIfOL, an IfO
algorithm that aims to learn a reward function that takes an agent trajectory
and compares it to an expert, providing rewards based on similarity to agent
behavior and implicit goal. We consider this reward function to be a distance
metric between trajectories of agent behavior and learn it via contrastive
learning. The contrastive learning objective aims to closely represent expert
trajectories and to distance them from non-expert trajectories. The set of
non-expert trajectories used in contrastive learning is made progressively more
complex by bootstrapping from roll-outs of the agent learned through RL using
the current reward function. We evaluate our approach on a variety of control
tasks showing that we can train effective policies using a limited number of
demonstrative trajectories, greatly improving on prior approaches that consider
raw observations.
Related papers
- Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement [50.481380478458945]
Iterative step-level Process Refinement (IPR) framework provides detailed step-by-step guidance to enhance agent training.
Our experiments on three complex agent tasks demonstrate that our framework outperforms a variety of strong baselines.
arXiv Detail & Related papers (2024-06-17T03:29:13Z) - Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents [49.85633804913796]
We present an exploration-based trajectory optimization approach, referred to as ETO.
This learning method is designed to enhance the performance of open LLM agents.
Our experiments on three complex tasks demonstrate that ETO consistently surpasses baseline performance by a large margin.
arXiv Detail & Related papers (2024-03-04T21:50:29Z) - Robust Visual Imitation Learning with Inverse Dynamics Representations [32.806294517277976]
We develop an inverse dynamics state representation learning objective to align the expert environment and the learning environment.
With the abstract state representation, we design an effective reward function, which thoroughly measures the similarity between behavior data and expert data.
Our approach can achieve a near-expert performance in most environments, and significantly outperforms the state-of-the-art visual IL methods and robust IL methods.
arXiv Detail & Related papers (2023-10-22T11:47:35Z) - Imitation Learning from Observation through Optimal Transport [25.398983671932154]
Imitation Learning from Observation (ILfO) is a setting in which a learner tries to imitate the behavior of an expert.
We show that existing methods can be simplified to generate a reward function without requiring learned models or adversarial learning.
We demonstrate the effectiveness of this simple approach on a variety of continuous control tasks and find that it surpasses the state of the art in the IlfO setting.
arXiv Detail & Related papers (2023-10-02T20:53:20Z) - Sequential Action-Induced Invariant Representation for Reinforcement
Learning [1.2046159151610263]
How to accurately learn task-relevant state representations from high-dimensional observations with visual distractions is a challenging problem in visual reinforcement learning.
We propose a Sequential Action-induced invariant Representation (SAR) method, in which the encoder is optimized by an auxiliary learner to only preserve the components that follow the control signals of sequential actions.
arXiv Detail & Related papers (2023-09-22T05:31:55Z) - SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models [22.472167814814448]
We propose a new model-based imitation learning algorithm named Separated Model-based Adversarial Imitation Learning (SeMAIL)
Our method achieves near-expert performance on various visual control tasks with complex observations and the more challenging tasks with different backgrounds from expert observations.
arXiv Detail & Related papers (2023-06-19T04:33:44Z) - Weakly-supervised HOI Detection via Prior-guided Bi-level Representation
Learning [66.00600682711995]
Human object interaction (HOI) detection plays a crucial role in human-centric scene understanding and serves as a fundamental building-block for many vision tasks.
One generalizable and scalable strategy for HOI detection is to use weak supervision, learning from image-level annotations only.
This is inherently challenging due to ambiguous human-object associations, large search space of detecting HOIs and highly noisy training signal.
We develop a CLIP-guided HOI representation capable of incorporating the prior knowledge at both image level and HOI instance level, and adopt a self-taught mechanism to prune incorrect human-object associations.
arXiv Detail & Related papers (2023-03-02T14:41:31Z) - Imitation by Predicting Observations [17.86983397979034]
We present a new method for imitation solely from observations that achieves comparable performance to experts on challenging continuous control tasks.
Our method, which we call FORM, is derived from an inverse RL objective and imitates using a model of expert behavior learned by generative modelling of the expert's observations.
We show that FORM performs comparably to a strong baseline IRL method (GAIL) on the DeepMind Control Suite benchmark, while outperforming GAIL in the presence of task-irrelevant features.
arXiv Detail & Related papers (2021-07-08T14:09:30Z) - Seeing Differently, Acting Similarly: Imitation Learning with
Heterogeneous Observations [126.78199124026398]
In many real-world imitation learning tasks, the demonstrator and the learner have to act in different but full observation spaces.
In this work, we model the above learning problem as Heterogeneous Observations Learning (HOIL)
We propose the Importance Weighting with REjection (IWRE) algorithm based on the techniques of importance-weighting, learning with rejection, and active querying to solve the key challenge of occupancy measure matching.
arXiv Detail & Related papers (2021-06-17T05:44:04Z) - PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via
Relabeling Experience and Unsupervised Pre-training [94.87393610927812]
We present an off-policy, interactive reinforcement learning algorithm that capitalizes on the strengths of both feedback and off-policy learning.
We demonstrate that our approach is capable of learning tasks of higher complexity than previously considered by human-in-the-loop methods.
arXiv Detail & Related papers (2021-06-09T14:10:50Z) - Reinforcement Learning with Prototypical Representations [114.35801511501639]
Proto-RL is a self-supervised framework that ties representation learning with exploration through prototypical representations.
These prototypes simultaneously serve as a summarization of the exploratory experience of an agent as well as a basis for representing observations.
This enables state-of-the-art downstream policy learning on a set of difficult continuous control tasks.
arXiv Detail & Related papers (2021-02-22T18:56:34Z)
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.