This repository provides code for Generative Adversarial Imitation Learning (GAIL), a method for learning policies from expert demonstrations. It is targeted at researchers and practitioners in reinforcement learning and robotics. The primary benefit is enabling agents to learn complex behaviors without explicit reward functions, relying solely on expert demonstrations.

How It Works

The implementation uses a Generative Adversarial Network (GAN) framework. A generator (the agent's policy) is trained to produce state-action sequences that are indistinguishable from those generated by an expert policy. A discriminator network learns to differentiate between expert and generated trajectories. This adversarial process drives the generator to mimic expert behavior. The underlying policy optimization is handled by Trust Region Policy Optimization (TRPO).

Quick Start & Requirements

• Install dependencies: pip install gym[mujoco] numpy scipy h5py tables pandas matplotlib
• Requires mujoco_py >= 0.4.0 and OpenAI Gym >= 0.1.0.
• Theano is a required backend, which may present installation challenges on modern systems.

Highlighted Details

• Implements Generative Adversarial Imitation Learning (GAIL).
• Includes an implementation of Trust Region Policy Optimization (TRPO).
• Provides expert policies trained via TRPO on true costs.
• Organizes experiment specifications and evaluation results.

Maintenance & Community

This project is archived and no updates are expected.

Licensing & Compatibility

The repository does not explicitly state a license. Given its origin within OpenAI and the lack of a LICENSE file, users should assume it is not open-source for commercial use without explicit permission.

Limitations & Caveats

The project is archived, indicating no ongoing development or support. The dependency on Theano, an older deep learning framework, may make installation and compatibility with current hardware and software stacks difficult.