Imitation Learning

509 papers with code • 0 benchmarks • 18 datasets

Imitation Learning is a framework for learning a behavior policy from demonstrations. Usually, demonstrations are presented in the form of state-action trajectories, with each pair indicating the action to take at the state being visited. In order to learn the behavior policy, the demonstrated actions are usually utilized in two ways. The first, known as Behavior Cloning (BC), treats the action as the target label for each state, and then learns a generalized mapping from states to actions in a supervised manner. Another way, known as Inverse Reinforcement Learning (IRL), views the demonstrated actions as a sequence of decisions, and aims at finding a reward/cost function under which the demonstrated decisions are optimal.

Finally, a newer methodology, Inverse Q-Learning aims at directly learning Q-functions from expert data, implicitly representing rewards, under which the optimal policy can be given as a Boltzmann distribution similar to soft Q-learning

Source: Learning to Imitate

Libraries

Use these libraries to find Imitation Learning models and implementations

Latest papers with no code

ELA: Exploited Level Augmentation for Offline Learning in Zero-Sum Games

no code yet • 28 Feb 2024

Offline learning has become widely used due to its ability to derive effective policies from offline datasets gathered by expert demonstrators without interacting with the environment directly.

Diffusion Meets DAgger: Supercharging Eye-in-hand Imitation Learning

no code yet • 27 Feb 2024

The Dataset Aggregation, or DAgger approach to this problem simply collects more data to cover these failure states.

Rethinking Mutual Information for Language Conditioned Skill Discovery on Imitation Learning

no code yet • 27 Feb 2024

Language-conditioned robot behavior plays a vital role in executing complex tasks by associating human commands or instructions with perception and actions.

C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory

no code yet • 26 Feb 2024

Generative Adversarial Imitation Learning (GAIL) trains a generative policy to mimic a demonstrator.

Learning Translations: Emergent Communication Pretraining for Cooperative Language Acquisition

no code yet • 26 Feb 2024

In Emergent Communication (EC) agents learn to communicate with one another, but the protocols that they develop are specialised to their training community.

Expressive Whole-Body Control for Humanoid Robots

no code yet • 26 Feb 2024

Can we enable humanoid robots to generate rich, diverse, and expressive motions in the real world?

Behavioral Refinement via Interpolant-based Policy Diffusion

no code yet • 25 Feb 2024

However, the target policy to be learned is often significantly different from Gaussian and this mismatch can result in poor performance when using a small number of diffusion steps (to improve inference speed) and under limited data.

CyberDemo: Augmenting Simulated Human Demonstration for Real-World Dexterous Manipulation

no code yet • 22 Feb 2024

We introduce CyberDemo, a novel approach to robotic imitation learning that leverages simulated human demonstrations for real-world tasks.

BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay

no code yet • 22 Feb 2024

Thus, we propose BeTAIL: Behavior Transformer Adversarial Imitation Learning, which combines a Behavior Transformer (BeT) policy from human demonstrations with online AIL.

Path Planning based on 2D Object Bounding-box

no code yet • 22 Feb 2024

The implementation of Autonomous Driving (AD) technologies within urban environments presents significant challenges.