Imitation
Clean PyTorch implementations of imitation and reward learning algorithms for reinforcement learning.
What is Imitation?
Imitation is a Python library that provides clean, production-ready implementations of imitation learning and reward learning algorithms. It solves the problem of training reinforcement learning agents efficiently by leveraging expert demonstrations, human preferences, or inferred reward functions, rather than relying solely on trial-and-error. The library includes algorithms like Behavioral Cloning, GAIL, AIRL, and Inverse Reinforcement Learning, all built with PyTorch.
Reinforcement learning researchers and practitioners who need reliable baselines for imitation learning, as well as developers working on AI systems that learn from human demonstrations or preferences, particularly in robotics and game AI.
Developers choose Imitation for its high-quality, well-documented implementations that are rigorously tested and benchmarked, offering a standardized toolkit that reduces implementation errors and accelerates research. Its modular design and support for both discrete and continuous environments make it versatile for various applications.
Overview
Clean PyTorch implementations of imitation and reward learning algorithms
Use Cases
Best For
- Training RL agents from expert demonstrations without hand-crafted rewards
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