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Getting Started

What is imitation?
Installation
First Steps
Command Line Interface

Main Concepts

Experts
Trajectories
Reward Networks
Limitations on Horizon Length
Benchmarking imitation
Benchmark Summary

Algorithms

Behavioral Cloning (BC)
Generative Adversarial Imitation Learning (GAIL)
Adversarial Inverse Reinforcement Learning (AIRL)
DAgger
Density-Based Reward Modeling
Maximum Causal Entropy Inverse Reinforcement Learning (MCE IRL)
Preference Comparisons
Soft Q Imitation Learning (SQIL)

Tutorials

Train an Agent using Behavior Cloning
Train an Agent using the DAgger Algorithm
Train an Agent using Generative Adversarial Imitation Learning
Train an Agent using Adversarial Inverse Reinforcement Learning
Learning a Reward Function using Preference Comparisons
Learning a Reward Function using Preference Comparisons on Atari
Learn a Reward Function using Maximum Conditional Entropy Inverse Reinforcement Learning
Learning a Reward Function using Kernel Density
Train an Agent using Soft Q Imitation Learning
Train an Agent using Soft Q Imitation Learning with SAC
Reliably compare algorithm performance
Train Behavior Cloning in a Custom Environment

API Reference

imitation
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- imitation.algorithms
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- imitation.data
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- imitation.policies
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- imitation.regularization
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  - imitation.regularization.regularizers
  - imitation.regularization.updaters
- imitation.rewards
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- imitation.scripts
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- imitation.testing
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- imitation.util
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Development

Developer Guide
Contributing
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- Code of Conduct
- Ways to contribute
Release Notes
License

v: latest

Versions: latest; stable; v1.0.0

Downloads: pdf; html; epub

On Read the Docs: Project Home; Builds

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imitation.scripts.config.train_imitation#

Configuration settings for train_dagger, training DAgger from synthetic demos.

imitation.scripts.config.train_preference_comparisons

imitation.scripts.config.train_adversarial

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